Demoing code for intake, fsspec, dask, and xarray libraries#
These libraries are used in most of the HyTEST notebooks and understanding how they work together will help understand the code more.
Author: Andrew Laws - USGS Web Informatics and Mapping (WIM)
Date: 3/20/2023
Focus: intake, fsspec, dask, and xarray libraries use in climate and forcings big data
# library imports
import fsspec
import hvplot.xarray
import intake
import os
import warnings
import rioxarray
import dask
from dask.distributed import LocalCluster, Client
from pygeohydro import pygeohydro
import xarray as xr
import geopandas as gpd
import pandas as pd
import geoviews as gv
import dask.dataframe as dd
warnings.filterwarnings('ignore')
Dask is a super useful Python package for parallel and lazy computing.#
When working with a large dataset that is multiple terrabytes in size such as CONUS404, Dask allows you to perform lazy operations, meaning the whole dataset is not read into memory (RAM) all at once but only once you’re ready for it. A prime example is work within HyTEST where we lazily ready in CONUS404, subset the data to certain variables and a limited geographic extend and then load it into memory. For a more in-depth dive and before you start to use it in-depth, make sure you read through the Dask documentation.
Another huge benefit of this: you can avoid the annoying step of downloading data to a local folder, instead just using a web service, saving time. This means if the base data is updated, you don’t have to download the dataset again and it is automatically available for your notebook.
Starting up dask on HPC or Nebari using a helper function#
def configure_cluster(machine):
''' Helper function to configure cluster
'''
if machine == 'denali':
from dask.distributed import LocalCluster, Client
cluster = LocalCluster(threads_per_worker=1)
client = Client(cluster)
elif machine == 'tallgrass':
from dask.distributed import Client
from dask_jobqueue import SLURMCluster
cluster = SLURMCluster(queue='cpu', cores=1, interface='ib0',
job_extra=['--nodes=1', '--ntasks-per-node=1', '--cpus-per-task=1'],
memory='6GB')
cluster.adapt(maximum_jobs=30)
client = Client(cluster)
elif machine == 'local':
import os
import warnings
from dask.distributed import LocalCluster, Client
warnings.warn("Running locally can result in costly data transfers!\n")
n_cores = os.cpu_count() # set to match your machine
cluster = LocalCluster(threads_per_worker=n_cores)
client = Client(cluster)
elif machine in ['esip-qhub-gateway-v0.4']:
import sys, os
sys.path.append(os.path.join(os.environ['HOME'],'shared','users','lib'))
import ebdpy as ebd
aws_profile = 'esip-qhub'
ebd.set_credentials(profile=aws_profile)
aws_region = 'us-west-2'
endpoint = f's3.{aws_region}.amazonaws.com'
ebd.set_credentials(profile=aws_profile, region=aws_region, endpoint=endpoint)
worker_max = 30
client,cluster = ebd.start_dask_cluster(profile=aws_profile, worker_max=worker_max,
region=aws_region, use_existing_cluster=True,
adaptive_scaling=False, wait_for_cluster=False,
worker_profile='Medium Worker', propagate_env=True)
return client, cluster
# if-else to determine HPC or Nebari
if 'SLURM_CLUSTER_NAME' in os.environ: #USGS HPC use SLURM CLUSTER to handle jobs, otherwise...
machine = os.environ['SLURM_CLUSTER_NAME']
cluster = configure_cluster(machine)
else: # use the Nebari machine
machine = 'esip-qhub-gateway-v0.4'
client, cluster = configure_cluster(machine)
Region: us-west-2
Existing Dask clusters:
Cluster Index c_idx: 0 / Name: dev.431da9ca68004398a4b06b214b3f86ba ClusterStatus.RUNNING
Using existing cluster [0].
Setting Fixed Scaling workers=30
Reconnect client to clear cache
client.dashboard_link (for new browser tab/window or dashboard searchbar in Jupyterhub):
https://nebari.esipfed.org/gateway/clusters/dev.431da9ca68004398a4b06b214b3f86ba/status
Propagating environment variables to workers
Using environment: users/users-pangeofu
You can also use Dask on your personal computer. This bit of Dask documentation explains how easy this is to set up.
# cluster = LocalCluster()
# client = Client(cluster)
With HyTEST, we use intake catalogs in our code to making data I/O more uniform.#
If we change where a dataset gets imported from, we only have to change it in one place rather than in each notebook. They can also be nested as you’ll see next.
Note: Select datasets that end in “onprem” if running on Denali/Tallgrass HPC or cloud data if working on QHub or local.
url = 'https://raw.githubusercontent.com/hytest-org/hytest/main/dataset_catalog/hytest_intake_catalog.yml'
cat = intake.open_catalog(url)
list(cat)
['conus404-drb-eval-tutorial-catalog',
'nhm-v1.0-daymet-catalog',
'nhm-v1.1-c404-bc-catalog',
'nhm-v1.1-gridmet-catalog',
'conus404-catalog',
'nwis-streamflow-usgs-gages-onprem',
'nwis-streamflow-usgs-gages-cloud',
'nwm21-streamflow-usgs-gages-onprem',
'nwm21-streamflow-usgs-gages-cloud',
'nwm21-streamflow-cloud',
'nwm21-scores',
'lcmap-cloud',
'rechunking-tutorial-cloud']
Notice the ‘conus404-drb-eval-tutorial-catalog’? That is a nested catalog for data we use in a specific tutorial series and can be accessed like this.
nested_cat = cat['conus404-drb-eval-tutorial-catalog']
list(nested_cat)
['conus404-drb-OSN',
'prism-drb-OSN',
'ceres-drb-OSN',
'crn-drb-OSN',
'hcn-drb-OSN']
And if you want to see the details about the dataset
nested_cat['conus404-drb-OSN']
conus404-drb-OSN:
args:
storage_options:
anon: true
client_kwargs:
endpoint_url: https://renc.osn.xsede.org
requester_pays: false
urlpath: s3://rsignellbucket2/hytest/tutorials/conus404_model_evaluation/c404_drb.nc
xarray_kwargs:
decode_coords: all
description: CONUS404 Delaware River Basin subset, 40 years of monthly data for
CONUS404 model evaluation
driver: intake_xarray.netcdf.NetCDFSource
metadata:
catalog_dir: https://raw.githubusercontent.com/hytest-org/hytest/main/dataset_catalog/subcatalogs
The other handy bit about intake is that you can open a dataset as a Dask data object easily (though the base class will be an xarray dataset). This is done lazily. But why the xarray dataset? From this read about the Dask ecosystem: “xarray: Wraps Dask Array, offering the same scalability, but with axis labels which add convenience when dealing with complex datasets.”
c404_drb = nested_cat['conus404-drb-OSN'].to_dask()
c404_drb
<xarray.Dataset>
Dimensions: (time: 504, y: 105, x: 47)
Coordinates:
lon (y, x) float32 ...
* x (x) float64 1.768e+06 1.772e+06 ... 1.948e+06 1.952e+06
* y (y) float64 2e+05 2.04e+05 2.08e+05 ... 6.12e+05 6.16e+05
lat (y, x) float32 ...
* time (time) datetime64[ns] 1979-10-31 1979-11-30 ... 2021-09-30
crs int32 ...
Data variables:
TK (time, y, x) float32 ...
RNET (time, y, x) float32 ...
PREC_ACC_NC (time, y, x) float32 ...
Attributes: (12/148)
AER_ANGEXP_OPT: 1
AER_ANGEXP_VAL: 1.2999999523162842
AER_AOD550_OPT: 1
AER_AOD550_VAL: 0.11999999731779099
AER_ASY_OPT: 1
AER_ASY_VAL: 0.8999999761581421
... ...
WEST-EAST_PATCH_START_STAG: 1
WEST-EAST_PATCH_START_UNSTAG: 1
W_DAMPING: 1
YSU_TOPDOWN_PBLMIX: 0
history: Tue Mar 29 16:35:22 2022: ncrcat -A -vW ...
history_of_appended_files: Tue Mar 29 16:35:22 2022: Appended file ...- time: 504
- y: 105
- x: 47
- lon(y, x)float32...
- description :
- LONGITUDE, WEST IS NEGATIVE
- long_name :
- Longitude, west is negative
- units :
- degree_east
[4935 values with dtype=float32]
- x(x)float641.768e+06 1.772e+06 ... 1.952e+06
- long_name :
- x coordinate of projection
- standard_name :
- projection_x_coordinate
- units :
- metre
- axis :
- X
array([1767902.098846, 1771902.098846, 1775902.098846, 1779902.098846, 1783902.098846, 1787902.098846, 1791902.098846, 1795902.098846, 1799902.098846, 1803902.098846, 1807902.098846, 1811902.098846, 1815902.098846, 1819902.098846, 1823902.098846, 1827902.098846, 1831902.098846, 1835902.098846, 1839902.098846, 1843902.098846, 1847902.098846, 1851902.098846, 1855902.098846, 1859902.098846, 1863902.098846, 1867902.098846, 1871902.098846, 1875902.098846, 1879902.098846, 1883902.098846, 1887902.098846, 1891902.098846, 1895902.098846, 1899902.098846, 1903902.098846, 1907902.098846, 1911902.098846, 1915902.098846, 1919902.098846, 1923902.098846, 1927902.098846, 1931902.098846, 1935902.098846, 1939902.098846, 1943902.098846, 1947902.098846, 1951902.098846]) - y(y)float642e+05 2.04e+05 ... 6.16e+05
- long_name :
- y coordinate of projection
- standard_name :
- projection_y_coordinate
- units :
- metre
- axis :
- Y
array([200039.100363, 204039.100363, 208039.100363, 212039.100363, 216039.100363, 220039.100363, 224039.100363, 228039.100363, 232039.100363, 236039.100363, 240039.100363, 244039.100363, 248039.100363, 252039.100363, 256039.100363, 260039.100363, 264039.100363, 268039.100363, 272039.100363, 276039.100363, 280039.100363, 284039.100363, 288039.100363, 292039.100363, 296039.100363, 300039.100363, 304039.100363, 308039.100363, 312039.100363, 316039.100363, 320039.100363, 324039.100363, 328039.100363, 332039.100363, 336039.100363, 340039.100363, 344039.100363, 348039.100363, 352039.100363, 356039.100363, 360039.100363, 364039.100363, 368039.100363, 372039.100363, 376039.100363, 380039.100363, 384039.100363, 388039.100363, 392039.100363, 396039.100363, 400039.100363, 404039.100363, 408039.100363, 412039.100363, 416039.100363, 420039.100363, 424039.100363, 428039.100363, 432039.100363, 436039.100363, 440039.100363, 444039.100363, 448039.100363, 452039.100363, 456039.100363, 460039.100363, 464039.100363, 468039.100363, 472039.100363, 476039.100363, 480039.100363, 484039.100363, 488039.100363, 492039.100363, 496039.100363, 500039.100363, 504039.100363, 508039.100363, 512039.100363, 516039.100363, 520039.100363, 524039.100363, 528039.100363, 532039.100363, 536039.100363, 540039.100363, 544039.100363, 548039.100363, 552039.100363, 556039.100363, 560039.100363, 564039.100363, 568039.100363, 572039.100363, 576039.100363, 580039.100363, 584039.100363, 588039.100363, 592039.100363, 596039.100363, 600039.100363, 604039.100363, 608039.100363, 612039.100363, 616039.100363]) - lat(y, x)float32...
- description :
- LATITUDE, SOUTH IS NEGATIVE
- long_name :
- Latitude, south is negative
- units :
- degree_north
[4935 values with dtype=float32]
- time(time)datetime64[ns]1979-10-31 ... 2021-09-30
array(['1979-10-31T00:00:00.000000000', '1979-11-30T00:00:00.000000000', '1979-12-31T00:00:00.000000000', ..., '2021-07-31T00:00:00.000000000', '2021-08-31T00:00:00.000000000', '2021-09-30T00:00:00.000000000'], dtype='datetime64[ns]') - crs()int32...
- grid_mapping_name :
- lambert_conformal_conic
- latitude_of_projection_origin :
- 39.100006103515625
- longitude_of_central_meridian :
- 262.0999984741211
- semi_major_axis :
- 6370000.0
- semi_minor_axis :
- 6370000.0
- standard_parallel :
- [30. 50.]
[1 values with dtype=int32]
- TK(time, y, x)float32...
- coordinates :
- XLONG XLAT XTIME
- description :
- MEAN AIR TEMPERATURE AT THE LOWEST MODEL LEVEL OVER THE PREVIOUS MONTH
- units :
- K
[2487240 values with dtype=float32]
- RNET(time, y, x)float32...
- description :
- MEAN RADIATION FROM PAST MONTH FOR BUCKET
- long_name :
- Bucket net radiation
- units :
- W m-2
[2487240 values with dtype=float32]
- PREC_ACC_NC(time, y, x)float32...
- description :
- ACCUMULATED GRID SCALE PRECIPITATION OVER prec_acc_dt PERIODS OF TIME
- integration_length :
- accumulated over prior month
- long_name :
- Accumulated grid scale precipitation
- units :
- mm
[2487240 values with dtype=float32]
- xPandasIndex
PandasIndex(Float64Index([1767902.098846458, 1771902.098846458, 1775902.098846458, 1779902.098846458, 1783902.098846458, 1787902.098846458, 1791902.098846458, 1795902.098846458, 1799902.098846458, 1803902.098846458, 1807902.098846458, 1811902.098846458, 1815902.098846458, 1819902.098846458, 1823902.098846458, 1827902.098846458, 1831902.098846458, 1835902.098846458, 1839902.098846458, 1843902.098846458, 1847902.098846458, 1851902.098846458, 1855902.098846458, 1859902.098846458, 1863902.098846458, 1867902.098846458, 1871902.098846458, 1875902.098846458, 1879902.098846458, 1883902.098846458, 1887902.098846458, 1891902.098846458, 1895902.098846458, 1899902.098846458, 1903902.098846458, 1907902.098846458, 1911902.098846458, 1915902.098846458, 1919902.098846458, 1923902.098846458, 1927902.098846458, 1931902.098846458, 1935902.098846458, 1939902.098846458, 1943902.098846458, 1947902.098846458, 1951902.098846458], dtype='float64', name='x')) - yPandasIndex
PandasIndex(Float64Index([200039.10036315513, 204039.10036315513, 208039.10036315513, 212039.10036315513, 216039.10036315513, 220039.10036315513, 224039.10036315513, 228039.10036315513, 232039.10036315513, 236039.10036315513, ... 580039.1003631551, 584039.1003631551, 588039.1003631551, 592039.1003631551, 596039.1003631551, 600039.1003631551, 604039.1003631551, 608039.1003631551, 612039.1003631551, 616039.1003631551], dtype='float64', name='y', length=105)) - timePandasIndex
PandasIndex(DatetimeIndex(['1979-10-31', '1979-11-30', '1979-12-31', '1980-01-31', '1980-02-29', '1980-03-31', '1980-04-30', '1980-05-31', '1980-06-30', '1980-07-31', ... '2020-12-31', '2021-01-31', '2021-02-28', '2021-03-31', '2021-04-30', '2021-05-31', '2021-06-30', '2021-07-31', '2021-08-31', '2021-09-30'], dtype='datetime64[ns]', name='time', length=504, freq=None))
- AER_ANGEXP_OPT :
- 1
- AER_ANGEXP_VAL :
- 1.2999999523162842
- AER_AOD550_OPT :
- 1
- AER_AOD550_VAL :
- 0.11999999731779099
- AER_ASY_OPT :
- 1
- AER_ASY_VAL :
- 0.8999999761581421
- AER_OPT :
- 1
- AER_SSA_OPT :
- 1
- AER_SSA_VAL :
- 0.8500000238418579
- AER_TYPE :
- 1
- BLDT :
- 0.0
- BL_PBL_PHYSICS :
- 1
- BOTTOM-TOP_GRID_DIMENSION :
- 51
- BOTTOM-TOP_PATCH_END_STAG :
- 51
- BOTTOM-TOP_PATCH_END_UNSTAG :
- 50
- BOTTOM-TOP_PATCH_START_STAG :
- 1
- BOTTOM-TOP_PATCH_START_UNSTAG :
- 1
- BUCKET_J :
- 1000000000.0
- BUCKET_MM :
- 100.0
- CEN_LAT :
- 39.100006103515625
- CEN_LON :
- -97.89999389648438
- CUDT :
- 5.0
- CU_PHYSICS :
- 0
- Contacts :
- CHANGHAI LIU (chliu@ucar.edu), KYOKO IKEDA (kyoko@ucar.edu)
- DAMPCOEF :
- 0.20000000298023224
- DAMP_OPT :
- 3
- DFI_OPT :
- 0
- DIFF_6TH_FACTOR :
- 0.11999999731779099
- DIFF_6TH_OPT :
- 0
- DIFF_OPT :
- 2
- DT :
- 20.0
- DTRAMP_MIN :
- 60.0
- DVEG :
- 9
- DX :
- 4000.0
- DY :
- 4000.0
- Division :
- NCAR/RAL/HAP
- ETAC :
- 0.0
- FEEDBACK :
- 1
- FGDT :
- 2.0
- FileGenerated :
- 20210204
- GFDDA_END_H :
- 999999
- GFDDA_INTERVAL_M :
- 180
- GMT :
- 0.0
- GPH :
- 4.999999873689376e-05
- GRAV_SETTLING :
- 0
- GRIDTYPE :
- C
- GRID_FDDA :
- 2
- GRID_ID :
- 1
- GRID_SFDDA :
- 0
- GT :
- 4.999999873689376e-05
- GUV :
- 4.999999873689376e-05
- GWD_OPT :
- 0
- HYBRID_OPT :
- -1
- HYPSOMETRIC_OPT :
- 2
- ICLOUD :
- 1
- ICLOUD_CU :
- 0
- IF_RAMPING :
- 1
- ISFFLX :
- 1
- ISFTCFLX :
- 0
- ISHALLOW :
- 0
- ISICE :
- 15
- ISLAKE :
- 21
- ISOILWATER :
- 14
- ISURBAN :
- 13
- ISWATER :
- 17
- I_PARENT_START :
- 1
- JULDAY :
- 274
- JULYR :
- 1979
- J_PARENT_START :
- 1
- KHDIF :
- 0.0
- KM_OPT :
- 4
- KVDIF :
- 0.0
- MAP_PROJ :
- 1
- MAP_PROJ_CHAR :
- Lambert Conformal
- MFSHCONV :
- 0
- MMINLU :
- MODIFIED_IGBP_MODIS_NOAH
- MOAD_CEN_LAT :
- 39.100006103515625
- MOIST_ADV_OPT :
- 1
- MP_PHYSICS :
- 8
- NCO :
- netCDF Operators version 4.9.5 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)
- NUM_LAND_CAT :
- 21
- OBS_NUDGE_OPT :
- 0
- OPT_ALB :
- 2
- OPT_BTR :
- 2
- OPT_CRS :
- 1
- OPT_FRZ :
- 1
- OPT_GLA :
- 1
- OPT_INF :
- 1
- OPT_RAD :
- 3
- OPT_RSF :
- 1
- OPT_RUN :
- 5
- OPT_SFC :
- 1
- OPT_SNF :
- 4
- OPT_STC :
- 3
- OPT_TBOT :
- 1
- PARENT_GRID_RATIO :
- 1
- PARENT_ID :
- 0
- POLE_LAT :
- 90.0
- POLE_LON :
- 0.0
- PREC_ACC_DT :
- 60.0
- Project :
- USGS CONUS404
- RADT :
- 5.0
- RA_LW_PHYSICS :
- 4
- RA_SW_PHYSICS :
- 4
- SCALAR_ADV_OPT :
- 1
- SCALAR_PBLMIX :
- 0
- SF_LAKE_PHYSICS :
- 0
- SF_OCEAN_PHYSICS :
- 0
- SF_SFCLAY_PHYSICS :
- 1
- SF_SURFACE_PHYSICS :
- 4
- SF_URBAN_PHYSICS :
- 0
- SGFDDA_END_H :
- 0
- SGFDDA_INTERVAL_M :
- 0
- SHCU_PHYSICS :
- 0
- SIMULATION_INITIALIZATION_TYPE :
- REAL-DATA CASE
- SIMULATION_START_DATE :
- 1979-10-01_00:00:00
- SKEBS_ON :
- 0
- SMOOTH_OPTION :
- 2
- SOUTH-NORTH_GRID_DIMENSION :
- 1016
- SOUTH-NORTH_PATCH_END_STAG :
- 1016
- SOUTH-NORTH_PATCH_END_UNSTAG :
- 1015
- SOUTH-NORTH_PATCH_START_STAG :
- 1
- SOUTH-NORTH_PATCH_START_UNSTAG :
- 1
- SPEC_BDY_FINAL_MU :
- 1
- SST_UPDATE :
- 1
- STAND_LON :
- -97.9000015258789
- START_DATE :
- 1979-10-01_00:00:00
- SURFACE_INPUT_SOURCE :
- 1
- SWINT_OPT :
- 0
- SWRAD_SCAT :
- 1.0
- Source_Code :
- make_conusii_2d.csh
- TITLE :
- OUTPUT FROM WRF V3.9.1.1 MODEL
- TKE_ADV_OPT :
- 1
- TOPO_WIND :
- 1
- TRACER_PBLMIX :
- 1
- TRUELAT1 :
- 30.0
- TRUELAT2 :
- 50.0
- USE_Q_DIABATIC :
- 0
- USE_THETA_M :
- 0
- WEST-EAST_GRID_DIMENSION :
- 1368
- WEST-EAST_PATCH_END_STAG :
- 1368
- WEST-EAST_PATCH_END_UNSTAG :
- 1367
- WEST-EAST_PATCH_START_STAG :
- 1
- WEST-EAST_PATCH_START_UNSTAG :
- 1
- W_DAMPING :
- 1
- YSU_TOPDOWN_PBLMIX :
- 0
- history :
- Tue Mar 29 16:35:22 2022: ncrcat -A -vW /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_W_d01_1979-10-01_00:00:00.nc /glade/scratch/kyoko/USGS/conus404_production_outputs/OUTPUT/WY1980/wrf2d_d01_1979-10-01_00:00:00 Tue Mar 29 16:35:21 2022: ncrcat -A -vZ /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_Z_d01_1979-10-01_00:00:00.nc /glade/scratch/kyoko/USGS/conus404_production_outputs/OUTPUT/WY1980/wrf2d_d01_1979-10-01_00:00:00
- history_of_appended_files :
- Tue Mar 29 16:35:22 2022: Appended file /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_W_d01_1979-10-01_00:00:00.nc had no "history" attribute Tue Mar 29 16:35:21 2022: Appended file /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_Z_d01_1979-10-01_00:00:00.nc had no "history" attribute
What is xarray?#
From the Xarray website: “Xarray introduces labels in the form of dimensions, coordinates and attributes on top of raw NumPy-like multidimensional arrays, which allows for a more intuitive, more concise, and less error-prone developer experience.”
If you’ve used numpy and pandas before, the API will be relatively familiar. As mentioned above, since it wraps a Dask array, it allows computation on large datasets in parallel. It can also take in many cloud and local file types. See this Xarray documentation about that. As with Dask, an upfront time investment in understanding Xarray will pay long-term efficiency rewards.
But what if you don’t want to use intake catalogs for a bunch of datasets you don’t maintain?
fsspec allows a user to connect with many different filesystems for querying, reading, and writing data.#
Want data from an AWS S3 bucket? fsspec has you covered, whether it requires credentials (requester pays and no anonymous read) or not (anonymous reads). Local files? Yep. Open data portals using something such as THREDDS Data Server? Still good. For a more in-depth look at fsspec, read through the fsspec documentation.
An additional benefit is that many libraries (dask, xarray, and pandas) already use fsspec in the background for reading and writing data. Next, you’ll see a couple examples of calling in data using fsspec directly and indirectly (through xarray), as well as incorporating dask.
First up, some data is only available through older file transfer protocal (FTP) methods. Using fsspec’s FTPFileSystem object.
from fsspec.implementations.ftp import FTPFileSystem
One thing to note: FTP server calls typically have a timeout, meaning if you take too long after instantiating the connecting it will disconnect. All you have to do is reconnect by running the code again (next cell). In the next few cells, we are reading in Climate Reference Network station data over an FTP connection.
First, create a FTP file system connection.
fs = FTPFileSystem("ftp.ncei.noaa.gov")
fs.ls("/pub/data/uscrn/products/")
[{'modify': '20230101154006',
'perm': 'flcdmpe',
'type': 'directory',
'unique': '29U7B0062A895',
'unix.group': '50',
'unix.mode': '02775',
'unix.owner': '14',
'name': '/pub/data/uscrn/products/daily01',
'size': 0},
{'modify': '20101215215611',
'perm': 'flcdmpe',
'type': 'directory',
'unique': '29U7B83636414',
'unix.group': '50',
'unix.mode': '02775',
'unix.owner': '14',
'name': '/pub/data/uscrn/products/hourly01',
'size': 0},
{'modify': '20230101021741',
'perm': 'flcdmpe',
'type': 'directory',
'unique': '29U7C0074877B',
'unix.group': '50',
'unix.mode': '02775',
'unix.owner': '14',
'name': '/pub/data/uscrn/products/hourly02',
'size': 0},
{'modify': '20220302211849',
'perm': 'flcdmpe',
'type': 'directory',
'unique': '29U7C80B4EA9E',
'unix.group': '50',
'unix.mode': '02775',
'unix.owner': '14',
'name': '/pub/data/uscrn/products/monthly01',
'size': 0},
{'modify': '20170705141338',
'perm': 'flcdmpe',
'type': 'directory',
'unique': '29U2CB9B4F',
'unix.group': '50',
'unix.mode': '02775',
'unix.owner': '14',
'name': '/pub/data/uscrn/products/previous_docs',
'size': 0},
{'modify': '20120613145107',
'perm': 'flcdmpe',
'type': 'directory',
'unique': '29U807515E5',
'unix.group': '50',
'unix.mode': '02775',
'unix.owner': '14',
'name': '/pub/data/uscrn/products/snapshots',
'size': 0},
{'modify': '20211219020507',
'perm': 'flcdmpe',
'type': 'directory',
'unique': '29U1007DBEBD',
'unix.group': '50',
'unix.mode': '02775',
'unix.owner': '14',
'name': '/pub/data/uscrn/products/soil01',
'size': 0},
{'modify': '20151005045036',
'perm': 'flcdmpe',
'type': 'directory',
'unique': '29U280984611',
'unix.group': '50',
'unix.mode': '02775',
'unix.owner': '14',
'name': '/pub/data/uscrn/products/soilsip01',
'size': 0},
{'modify': '20230101015409',
'perm': 'flcdmpe',
'type': 'directory',
'unique': '29U3007AB113',
'unix.group': '50',
'unix.mode': '02775',
'unix.owner': '14',
'name': '/pub/data/uscrn/products/subhourly01',
'size': 0},
{'modify': '20170630183509',
'perm': 'adfrw',
'size': 4242,
'type': 'file',
'unique': '29U1E0110F37C',
'unix.group': '50',
'unix.mode': '0664',
'unix.owner': '14',
'name': '/pub/data/uscrn/products/DATASET-STATUS.txt'},
{'modify': '20130715195333',
'perm': 'adfrw',
'size': 855,
'type': 'file',
'unique': '29U1E0110F37D',
'unix.group': '50',
'unix.mode': '0664',
'unix.owner': '14',
'name': '/pub/data/uscrn/products/rss.xml'},
{'modify': '20210830171807',
'perm': 'fle',
'type': 'directory',
'unique': '29U4D0063872E',
'unix.group': '50',
'unix.mode': '02775',
'unix.owner': '14',
'name': '/pub/data/uscrn/products/.heat01',
'size': 0},
{'modify': '20211123170434',
'perm': 'fle',
'type': 'directory',
'unique': '29U18021845E',
'unix.group': '50',
'unix.mode': '02775',
'unix.owner': '14',
'name': '/pub/data/uscrn/products/heat01',
'size': 0},
{'modify': '20211217212112',
'perm': 'fle',
'type': 'directory',
'unique': '29U4C8157522C',
'unix.group': '50',
'unix.mode': '02775',
'unix.owner': '14',
'name': '/pub/data/uscrn/products/drought01',
'size': 0},
{'modify': '20211206050009',
'perm': 'adfr',
'size': 36542,
'type': 'file',
'unique': '29U1E0402940C',
'unix.group': '50',
'unix.mode': '0664',
'unix.owner': '14',
'name': '/pub/data/uscrn/products/stations.tsv'},
{'modify': '20211206232838',
'perm': 'fle',
'type': 'directory',
'unique': '29U5981C8AF35',
'unix.group': '50',
'unix.mode': '02775',
'unix.owner': '14',
'name': '/pub/data/uscrn/products/soil',
'size': 0}]
Since the file type is tab-separated values (tsv), we will use the pd.read_table function to create a Dataframe
uscrn_all = pd.read_table(fs.open("/pub/data/uscrn/products/stations.tsv"))
uscrn_all.head()
| WBAN | COUNTRY | STATE | LOCATION | VECTOR | NAME | LATITUDE | LONGITUDE | ELEVATION | STATUS | COMMISSIONING | CLOSING | OPERATION | PAIRING | NETWORK | STATION_ID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 03047 | US | TX | Monahans | 6 ENE | Sandhills State Park | 31.62 | -102.80 | 2724 | Commissioned | 2004-01-11 19:00:00.0 | NaN | Operational | NaN | USCRN | 1019 |
| 1 | 03048 | US | NM | Socorro | 20 N | Sevilleta National Wildlife Refuge (LTER Site) | 34.35 | -106.88 | 4847 | Commissioned | 2004-01-11 19:00:00.0 | NaN | Operational | NaN | USCRN | 1020 |
| 2 | 03054 | US | TX | Muleshoe | 19 S | Muleshoe National Wildlife Refuge (Headquarter... | 33.95 | -102.77 | 3742 | Commissioned | 2004-04-22 20:00:00.0 | NaN | Operational | NaN | USCRN | 1067 |
| 3 | 03055 | US | OK | Goodwell | 2 E | OK Panhandle Research & Extn. Center (Native ... | 36.59 | -101.59 | 3266 | Commissioned | 2004-04-22 20:00:00.0 | NaN | Operational | NaN | USCRN | 1068 |
| 4 | 03060 | US | CO | Montrose | 11 ENE | Black Canyon of the Gunnison National Park (Ve... | 38.54 | -107.69 | 8402 | Commissioned | 2004-09-07 20:00:00.0 | NaN | Operational | NaN | USCRN | 1109 |
What about reading from a permissioned S3 bucket? Lets find the URL for a dataset from the intake catalog. Note: this requires you to have AWS credentials.
If you are unsure of what permissioned/requester pays bucket is, read this resource.
conus404_cat = cat["conus404_catalog"]
conus404_cat['conus404-hourly-cloud']
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[12], line 1
----> 1 conus404_cat = cat["conus404_catalog"]
2 conus404_cat['conus404-hourly-cloud']
File /home/conda/users/ef214d1986de9477d5223419ba4838d76e9839b19e4fb02c9e208c97cab5a61b-20230329-131713-443303-165-pangeofu/lib/python3.10/site-packages/intake/catalog/base.py:475, in Catalog.__getitem__(self, key)
473 out = out[part]
474 return out()
--> 475 raise KeyError(key)
KeyError: 'conus404_catalog'
Using the urlpath in xarray, we can read in the dataset using saved AWS credentials and the fsspec.get_mapper() method to pass the correct parameters to xarray.open_zarr().
c404_hourly_url = "s3://nhgf-development/conus404/conus404_hourly_202209.zarr"
fs_gm = fsspec.get_mapper(c404_hourly_url, # given the url, fsspec will know it is speaking to an S3 file system
anon=False,
requester_pays=True, # our S3 credentials will be charged
client_kwargs={'region_name':'us-west-2'})
# open dataset
c404_hourly = xr.open_zarr(fs_gm)
c404_hourly
<xarray.Dataset>
Dimensions: (time: 368064, y: 1015, x: 1367, bottom_top_stag: 51,
bottom_top: 50, soil_layers_stag: 4, x_stag: 1368,
y_stag: 1016, snow_layers_stag: 3, snso_layers_stag: 7)
Coordinates:
lat (y, x) float32 dask.array<chunksize=(175, 175), meta=np.ndarray>
lat_u (y, x_stag) float32 dask.array<chunksize=(175, 175), meta=np.ndarray>
lat_v (y_stag, x) float32 dask.array<chunksize=(175, 175), meta=np.ndarray>
lon (y, x) float32 dask.array<chunksize=(175, 175), meta=np.ndarray>
lon_u (y, x_stag) float32 dask.array<chunksize=(175, 175), meta=np.ndarray>
lon_v (y_stag, x) float32 dask.array<chunksize=(175, 175), meta=np.ndarray>
* time (time) datetime64[ns] 1979-10-01 ... 2021-09-25T23:00:00
* x (x) float64 -2.732e+06 -2.728e+06 ... 2.728e+06 2.732e+06
* y (y) float64 -2.028e+06 -2.024e+06 ... 2.024e+06 2.028e+06
Dimensions without coordinates: bottom_top_stag, bottom_top, soil_layers_stag,
x_stag, y_stag, snow_layers_stag,
snso_layers_stag
Data variables: (12/157)
ACDEWC (time, y, x) float32 dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
ACDRIPR (time, y, x) float32 dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
ACDRIPS (time, y, x) float32 dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
ACECAN (time, y, x) float32 dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
ACEDIR (time, y, x) float32 dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
ACETLSM (time, y, x) float32 dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
... ...
ZNU (bottom_top) float32 dask.array<chunksize=(50,), meta=np.ndarray>
ZNW (bottom_top_stag) float32 dask.array<chunksize=(51,), meta=np.ndarray>
ZS (soil_layers_stag) float32 dask.array<chunksize=(4,), meta=np.ndarray>
ZSNSO (time, snso_layers_stag, y, x) float32 dask.array<chunksize=(144, 7, 175, 175), meta=np.ndarray>
ZWT (time, y, x) float32 dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
crs int32 ...
Attributes: (12/148)
AER_ANGEXP_OPT: 1
AER_ANGEXP_VAL: 1.2999999523162842
AER_AOD550_OPT: 1
AER_AOD550_VAL: 0.11999999731779099
AER_ASY_OPT: 1
AER_ASY_VAL: 0.8999999761581421
... ...
WEST-EAST_PATCH_START_STAG: 1
WEST-EAST_PATCH_START_UNSTAG: 1
W_DAMPING: 1
YSU_TOPDOWN_PBLMIX: 0
history: Tue Mar 29 16:35:22 2022: ncrcat -A -vW ...
history_of_appended_files: Tue Mar 29 16:35:22 2022: Appended file ...- time: 368064
- y: 1015
- x: 1367
- bottom_top_stag: 51
- bottom_top: 50
- soil_layers_stag: 4
- x_stag: 1368
- y_stag: 1016
- snow_layers_stag: 3
- snso_layers_stag: 7
- lat(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LATITUDE, SOUTH IS NEGATIVE
- long_name :
- Latitude, south is negative
- standard_name :
- latitude
- units :
- degree_north
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - lat_u(y, x_stag)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LATITUDE, SOUTH IS NEGATIVE
- long_name :
- Latitude, south is negative
- units :
- degree_north
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1015, 1368) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - lat_v(y_stag, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LATITUDE, SOUTH IS NEGATIVE
- long_name :
- Latitude, south is negative
- units :
- degree_north
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1016, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - lon(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LONGITUDE, WEST IS NEGATIVE
- long_name :
- Longitude, west is negative
- standard_name :
- longitude
- units :
- degree_east
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - lon_u(y, x_stag)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LONGITUDE, WEST IS NEGATIVE
- long_name :
- Longitude, west is negative
- units :
- degree_east
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1015, 1368) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - lon_v(y_stag, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LONGITUDE, WEST IS NEGATIVE
- long_name :
- Longitude, west is negative
- units :
- degree_east
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1016, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - time(time)datetime64[ns]1979-10-01 ... 2021-09-25T23:00:00
- standard_name :
- time
array(['1979-10-01T00:00:00.000000000', '1979-10-01T01:00:00.000000000', '1979-10-01T02:00:00.000000000', ..., '2021-09-25T21:00:00.000000000', '2021-09-25T22:00:00.000000000', '2021-09-25T23:00:00.000000000'], dtype='datetime64[ns]') - x(x)float64-2.732e+06 -2.728e+06 ... 2.732e+06
- long_name :
- x coordinate of projection
- standard_name :
- projection_x_coordinate
- units :
- m
array([-2732097.901154, -2728097.901154, -2724097.901154, ..., 2723902.098846, 2727902.098846, 2731902.098846]) - y(y)float64-2.028e+06 -2.024e+06 ... 2.028e+06
- long_name :
- y coordinate of projection
- standard_name :
- projection_y_coordinate
- units :
- m
array([-2027960.899637, -2023960.899637, -2019960.899637, ..., 2020039.100363, 2024039.100363, 2028039.100363])
- ACDEWC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QDEWC
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy dew rate
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACDRIPR(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QDRIPR
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy precipitation drip rate
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACDRIPS(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QDRIPS
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy snow drip rate
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACECAN(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated ECAN
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated net evaporation of canopy water (evap + sublim - dew - frost)
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACEDIR(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated EDIR
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated net soil evaporation or snowpack sublimation (evap or sublim - dew or frost)
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACETLSM(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated ET
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated total evaporation
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACETRAN(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated ETRAN
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated plant transpiration
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACEVAC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QEVAC
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy evaporation
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACEVB(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated EVB
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated latent heat flux over bare ground
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACEVC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated EVC
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated latent heat flux for canopy layer
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACEVG(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated EVG
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated ground latent heat flux below canopy
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACFROC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QFROC
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy frost
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACFRZC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QFRZC
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated refreezing of canopy liquid water
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACGHB(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated GHB
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated heat flux into soil or snowpack for bare ground
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACGHFLSM(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated total ground heat flux
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated total ground heat flux into soil or snowpack
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACGHV(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated GHV
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated heat flux into soil or snowpack under canopy
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACINTR(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QINTR
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy rain interception rate
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACINTS(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QINTS
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy snow interception rate
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACLHFLSM(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated total latent heat flux
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated total latent heat flux
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACLWDNB(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED DOWNWELLING LONGWAVE FLUX AT BOTTOM
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated downwelling longwave radiation flux at bottom
- units :
- J m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACLWUPB(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED UPWELLING LONGWAVE FLUX AT BOTTOM
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated upwelling longwave radiation flux at bottom
- units :
- J m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACMELTC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QMELTC
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy snow melt
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACPONDING(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated PONDING
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated surface ponding from complete pack melt
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACQLAT(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- accumulated lateral flow
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated groundwater lateral flow
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACQRF(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- accumulated baseflow
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated groundwater baseflow
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACRAINLSM(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated RAINLSM
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated liquid precipitation into land surface model
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACRAINSNOW(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated rain on snow pack
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated rain on snow pack
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACRUNSB(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated RUNSB
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated subsurface runoff
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACRUNSF(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated RUNSF
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated surface runoff
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSHFLSM(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated total sensible heat flux
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated total sensible heat flux
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSNBOT(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QSNBOT
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated liquid water flux out of bottom of snowpack
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSNFRO(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QSNFRO
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated snowpack frost
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSNOM(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED MELTED SNOW
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated total liquid water out of the snowpack
- units :
- kg m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSNOWLSM(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated SNOWLSM
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated frozen precipitation into land surface model
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSNSUB(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QSNSUB
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated snowpack sublimation
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSUBC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QSUBC
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy snow sublimation
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSWDNB(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED DOWNWELLING SHORTWAVE FLUX AT BOTTOM
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated downwelling shortwave radiation flux at bottom
- units :
- J m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSWDNLSM(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated SWDN
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated shortwave radiation down at land surface model
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSWDNT(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED DOWNWELLING SHORTWAVE FLUX AT TOP
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated downwelling shortwave radiation flux at top
- units :
- J m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSWUPB(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED UPWELLING SHORTWAVE FLUX AT BOTTOM
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated upwelling shortwave radiation flux at bottom
- units :
- J m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSWUPLSM(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated SWUP
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated shortwave radiation up at land surface model
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACTHROR(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QTHROR
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy rain throughfall
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACTHROS(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QTHROS
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy snow throughfall
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACTR(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated TR
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated transpiration
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ALBEDO(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ALBEDO
- grid_mapping :
- crs
- long_name :
- Surface albedo including snow effects
- number_of_significant_digits :
- 5
- units :
- 1
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - BF(bottom_top_stag)float32dask.array<chunksize=(51,), meta=np.ndarray>
- description :
- full levels, bf=0 => isobaric; bf=znw => sigma
- long_name :
- Full levels, bf=0 => isobaric; bf=znw => sigma
- units :
- Dimensionless
Array Chunk Bytes 204 B 204 B Shape (51,) (51,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - BH(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
- half levels, bh=0 => isobaric; bh=znu => sigma
- long_name :
- Half levels, bh=0 => isobaric; bh=znu => sigma
- units :
- Dimensionless
Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - C1F(bottom_top_stag)float32dask.array<chunksize=(51,), meta=np.ndarray>
- description :
- full levels, c1f = d bf / d eta, using znu
- long_name :
- Full levels, c1f = d bf / d eta, using znu
- units :
- Dimensionless
Array Chunk Bytes 204 B 204 B Shape (51,) (51,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - C1H(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
- half levels, c1h = d bf / d eta, using znw
- long_name :
- Half levels, c1h = d bf / d eta, using znw
- units :
- Dimensionless
Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - C2F(bottom_top_stag)float32dask.array<chunksize=(51,), meta=np.ndarray>
- description :
- full levels, c2f = (1-c1f)*(p0-pt)
- long_name :
- Full levels, c2f = (1-c1f)*(p0-pt)
- units :
- Pa
Array Chunk Bytes 204 B 204 B Shape (51,) (51,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - C2H(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
- half levels, c2h = (1-c1h)*(p0-pt)
- long_name :
- Half levels, c2h = (1-c1h)*(p0-pt)
- units :
- Pa
Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - C3F(bottom_top_stag)float32dask.array<chunksize=(51,), meta=np.ndarray>
- description :
- full levels, c3f = bf
- long_name :
- Full levels, c3f = bf
- units :
- Dimensionless
Array Chunk Bytes 204 B 204 B Shape (51,) (51,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - C3H(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
- half levels, c3h = bh
- long_name :
- Half levels, c3h = bh
- units :
- Dimensionless
Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - C4F(bottom_top_stag)float32dask.array<chunksize=(51,), meta=np.ndarray>
- description :
- full levels, c4f = (eta-bf)*(p0-pt)+pt, using znw
- long_name :
- Full levels, c4f = (eta-bf)*(p0-pt)+pt, using znw
- units :
- Pa
Array Chunk Bytes 204 B 204 B Shape (51,) (51,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - C4H(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
- half levels, c4h = (eta-bh)*(p0-pt)+pt, using znu
- long_name :
- Half levels, c4h = (eta-bh)*(p0-pt)+pt, using znu
- units :
- Pa
Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - CANWAT(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- CANOPY WATER
- grid_mapping :
- crs
- long_name :
- Canopy intercepted water
- units :
- kg m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - CF1()float32...
- description :
- 2nd order extrapolation constant
- long_name :
- 2nd order extrapolation constant
- units :
[1 values with dtype=float32]
- CF2()float32...
- description :
- 2nd order extrapolation constant
- long_name :
- 2nd order extrapolation constant
- units :
[1 values with dtype=float32]
- CF3()float32...
- description :
- 2nd order extrapolation constant
- long_name :
- 2nd order extrapolation constant
- units :
[1 values with dtype=float32]
- CFN()float32...
- description :
- extrapolation constant
- long_name :
- Extrapolation constant
- units :
[1 values with dtype=float32]
- CFN1()float32...
- description :
- extrapolation constant
- long_name :
- Extrapolation constant
- units :
[1 values with dtype=float32]
- CLAT(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- COMPUTATIONAL GRID LATITUDE, SOUTH IS NEGATIVE
- grid_mapping :
- crs
- long_name :
- Computational grid latitude, south is negative
- units :
- degree_north
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - COSALPHA(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- Local cosine of map rotation
- grid_mapping :
- crs
- long_name :
- Local cosine of map rotation
- units :
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - DN(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
- d(eta) values between half (mass) levels
- long_name :
- D(eta) values between half (mass) levels
- units :
Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - DNW(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
- d(eta) values between full (w) levels
- long_name :
- D(eta) values between full (w) levels
- units :
Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - DZS(soil_layers_stag)float32dask.array<chunksize=(4,), meta=np.ndarray>
- description :
- THICKNESSES OF SOIL LAYERS
- long_name :
- Thickness of soil layers
- units :
- m
Array Chunk Bytes 16 B 16 B Shape (4,) (4,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - E(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- Coriolis cosine latitude term
- grid_mapping :
- crs
- long_name :
- Coriolis cosine latitude term
- units :
- s-1
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - E2(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- grid_mapping :
- crs
- long_name :
- Vapor pressure at 2 meters
- units :
- Pa
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ES2(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- grid_mapping :
- crs
- long_name :
- Saturation vapor pressure at 2 meters
- notes :
- Tetens equation
- units :
- Pa
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - F(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- Coriolis sine latitude term
- grid_mapping :
- crs
- long_name :
- Coriolis sine latitude term
- units :
- s-1
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - FNM(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
- upper weight for vertical stretching
- grid_mapping :
- crs
- long_name :
- Upper weight for vertical stretching
- units :
Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - FNP(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
- lower weight for vertical stretching
- long_name :
- Lower weight for vertical stretching
- units :
Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - GRAUPEL_ACC_NC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED GRAUPEL WATER EQUIVALENT OVER prec_acc_dt PERIODS OF TIME
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated graupel water equivalent
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - HGT(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- Terrain Height
- grid_mapping :
- crs
- long_name :
- Terrain Height
- units :
- m
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - ISLTYP(y, x)int32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- DOMINANT SOIL CATEGORY
- flag_meanings :
- sand loamy_sand sand_loam silt_loam silt loam sandy_clay_loam silty_clay_loam clay_loam sandy_clay silty_clay clay organic_material water bedrock other_land-ice
- flag_values :
- 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
- grid_mapping :
- crs
- long_name :
- Dominant soil category
- units :
- valid_range :
- 1, 16
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type int32 numpy.ndarray - IVGTYP(y, x)int32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- DOMINANT VEGETATION CATEGORY
- flag_meanings :
- evergreen_needleleaf_forest evergreen_broadleaf_forest deciduous_needleleaf_forest deciduous_broadleaf_forest mixed_forests closed_shrublands open_shrublands woody_savannas savannas grasslands permanent_wetlands croplands urban_and_built-up cropland-natural_vegetation_mosaic snow_and_ice barren_or_sparsely_vegetated water wooded_tundra mixed_tundra barren_tundra
- flag_values :
- 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
- grid_mapping :
- crs
- long_name :
- Dominant vegetation category
- units :
- valid_range :
- 1, 20
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type int32 numpy.ndarray - LAKEMASK(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LAKE MASK (1 FOR LAKE, 0 FOR NON-LAKE)
- flag_meanings :
- non-lake lake
- flag_values :
- 0.0, 1.0
- grid_mapping :
- crs
- long_name :
- Lake mask (1 for lake, 0 for non-lake)
- units :
- valid_range :
- 0.0, 1.0
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - LANDMASK(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LAND MASK (1 FOR LAND, 0 FOR WATER)
- flag_meanings :
- water land
- flag_values :
- 0.0, 1.0
- grid_mapping :
- crs
- long_name :
- Land mask (1 for land, 0 for water)
- units :
- valid_range :
- 0.0, 1.0
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - LU_INDEX(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LAND USE CATEGORY
- flag_meanings :
- evergreen_needleleaf_forest evergreen_broadleaf_forest deciduous_needleleaf_forest deciduous_broadleaf_forest mixed_forests closed_shrublands open_shrublands woody_savannas savannas grasslands permanent_wetlands croplands urban_and_built-up cropland-natural_vegetation_mosaic snow_and_ice barren_or_sparsely_vegetated water wooded_tundra mixed_tundra barren_tundra
- flag_values :
- 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
- grid_mapping :
- crs
- long_name :
- Land use category
- units :
- valid_range :
- 1, 20
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAPFAC_M(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- Map scale factor on mass grid
- grid_mapping :
- crs
- long_name :
- Map scale factor on mass grid
- units :
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAPFAC_MX(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- Map scale factor on mass grid, x direction
- grid_mapping :
- crs
- long_name :
- Map scale factor on mass grid, x direction
- units :
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAPFAC_MY(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- Map scale factor on mass grid, y direction
- grid_mapping :
- crs
- long_name :
- Map scale factor on mass grid, y direction
- units :
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAPFAC_U(y, x_stag)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- Map scale factor on u-grid
- grid_mapping :
- crs
- long_name :
- Map scale factor on u-grid
- units :
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1015, 1368) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAPFAC_UX(y, x_stag)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- Map scale factor on u-grid, x direction
- grid_mapping :
- crs
- long_name :
- Map scale factor on u-grid, x direction
- units :
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1015, 1368) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAPFAC_UY(y, x_stag)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- Map scale factor on u-grid, y direction
- grid_mapping :
- crs
- long_name :
- Map scale factor on u-grid, y direction
- units :
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1015, 1368) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAPFAC_V(y_stag, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- Map scale factor on v-grid
- grid_mapping :
- crs
- long_name :
- Map scale factor on v-grid
- units :
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1016, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAPFAC_VX(y_stag, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- Map scale factor on v-grid, x direction
- grid_mapping :
- crs
- long_name :
- Map scale factor on v-grid, x direction
- units :
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1016, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAPFAC_VY(y_stag, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- Map scale factor on v-grid, y direction
- grid_mapping :
- crs
- long_name :
- Map scale factor on v-grid, y direction
- units :
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1016, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAX_MSTFX()float32...
- description :
- Max map factor in domain
- long_name :
- Maximum map factor in domain
- units :
[1 values with dtype=float32]
- MAX_MSTFY()float32...
- description :
- Max map factor in domain
- long_name :
- Maximum map factor in domain
- units :
[1 values with dtype=float32]
- MF_VX_INV(y_stag, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- Inverse map scale factor on v-grid, x direction
- grid_mapping :
- crs
- long_name :
- Inverse map scale factor on v-grid, x direction
- units :
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1016, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MLCAPE(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- MIXED-LAYER CAPE
- grid_mapping :
- crs
- long_name :
- Mixed-layer convective available potential energy (CAPE)
- units :
- J kg-1
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - MUB(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- base state dry air mass in column
- grid_mapping :
- crs
- long_name :
- Base state dry air mass in column
- units :
- Pa
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - P00()float32...
- description :
- BASE STATE PRESURE
- long_name :
- Base state pressure
- units :
- Pa
[1 values with dtype=float32]
- PB(bottom_top, y, x)float32dask.array<chunksize=(50, 175, 175), meta=np.ndarray>
- description :
- BASE STATE PRESSURE
- grid_mapping :
- crs
- long_name :
- Base state pressure
- units :
- Pa
Array Chunk Bytes 264.65 MiB 5.84 MiB Shape (50, 1015, 1367) (50, 175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - PBLH(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- PBL HEIGHT
- grid_mapping :
- crs
- long_name :
- Planetary boundary layer height
- units :
- m
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - PHB(bottom_top_stag, y, x)float32dask.array<chunksize=(51, 175, 175), meta=np.ndarray>
- description :
- base-state geopotential
- grid_mapping :
- crs
- long_name :
- Base-state geopotential
- units :
- m2 s-2
Array Chunk Bytes 269.94 MiB 5.96 MiB Shape (51, 1015, 1367) (51, 175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - PREC_ACC_NC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED GRID SCALE PRECIPITATION OVER prec_acc_dt PERIODS OF TIME
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated grid scale precipitation
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - PSFC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- SFC PRESSURE
- grid_mapping :
- crs
- long_name :
- Surface pressure
- units :
- Pa
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - PWAT(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- coordinates :
- XLONG XLAT XTIME
- description :
- Precipitable Water
- sum_op_ncl :
- dim_sum_n over dimension(s): bottom_top
- units :
- meters
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - P_STRAT()float32...
- description :
- BASE STATE PRESSURE AT BOTTOM OF STRATOSPHERE
- long_name :
- Base state pressure at bottom of stratosphere
- units :
- Pa
[1 values with dtype=float32]
- P_TOP()float32...
- description :
- PRESSURE TOP OF THE MODEL
- long_name :
- Pressure top of the model
- units :
- Pa
[1 values with dtype=float32]
- Q2(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- QV at 2 M
- grid_mapping :
- crs
- long_name :
- Water vapor mixing ratio at 2 meters
- units :
- kg kg-1
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - QRFS(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- sum baseflow
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated baseflow
- units :
- m
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - QSLAT(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- sum lateral flow
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated groundwater lateral flow
- units :
- m
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - QSPRINGS(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- sum seeping water
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated seeping water
- units :
- m
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - QVAPOR(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- Water vapor mixing ratio
- grid_mapping :
- crs
- long_name :
- Water vapor mixing ratio
- units :
- kg kg-1
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - RDN(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
- inverse d(eta) values between half (mass) levels
- long_name :
- Inverse d(eta) values between half (mass) levels
- units :
Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - RDNW(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
- inverse d(eta) values between full (w) levels
- long_name :
- Inverse d(eta) values between full (w) levels
- units :
Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - RDX()float32...
- description :
- INVERSE X GRID LENGTH
- long_name :
- Inverse X grid length
- units :
- m-1
[1 values with dtype=float32]
- RDY()float32...
- description :
- INVERSE Y GRID LENGTH
- long_name :
- Inverse Y grid length
- units :
- m-1
[1 values with dtype=float32]
- RECH(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- sum water table recharge
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated water table recharge
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - RH2(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- grid_mapping :
- crs
- long_name :
- Relative humidity at 2 meters
- notes :
- Tetens equation
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SH2(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- grid_mapping :
- crs
- long_name :
- Specific humidity at 2 meters
- units :
- kg kg-1
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SH2O(time, soil_layers_stag, y, x)float32dask.array<chunksize=(144, 4, 175, 175), meta=np.ndarray>
- description :
- SOIL LIQUID WATER
- grid_mapping :
- crs
- long_name :
- Soil liquid water
- number_of_significant_digits :
- 5
- units :
- m3 m-3
Array Chunk Bytes 7.43 TiB 67.29 MiB Shape (368064, 4, 1015, 1367) (144, 4, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SHDMAX(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- ANNUAL MAX VEG FRACTION
- grid_mapping :
- crs
- long_name :
- Annual maximum vegetation fraction
- units :
- 1
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - SHDMIN(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- ANNUAL MIN VEG FRACTION
- grid_mapping :
- crs
- long_name :
- Annual minimum vegetation fraction
- units :
- 1
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - SINALPHA(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- Local sine of map rotation
- grid_mapping :
- crs
- long_name :
- Local sine of map rotation
- units :
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - SMCWTD(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- deep soil moisture
- grid_mapping :
- crs
- long_name :
- Deep soil moisture
- units :
- m3 m-3
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SMOIS(time, soil_layers_stag, y, x)float32dask.array<chunksize=(144, 4, 175, 175), meta=np.ndarray>
- description :
- SOIL MOISTURE
- grid_mapping :
- crs
- long_name :
- Soil moisture
- number_of_significant_digits :
- 5
- units :
- m3 m-3
Array Chunk Bytes 7.43 TiB 67.29 MiB Shape (368064, 4, 1015, 1367) (144, 4, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SNICE(time, snow_layers_stag, y, x)float32dask.array<chunksize=(144, 3, 175, 175), meta=np.ndarray>
- description :
- snow layer ice
- grid_mapping :
- crs
- long_name :
- Snow layer ice
- units :
- mm
Array Chunk Bytes 5.57 TiB 50.47 MiB Shape (368064, 3, 1015, 1367) (144, 3, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SNLIQ(time, snow_layers_stag, y, x)float32dask.array<chunksize=(144, 3, 175, 175), meta=np.ndarray>
- description :
- snow layer liquid
- grid_mapping :
- crs
- long_name :
- Snow layer liquid
- units :
- mm
Array Chunk Bytes 5.57 TiB 50.47 MiB Shape (368064, 3, 1015, 1367) (144, 3, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SNOALB(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- ANNUAL MAX SNOW ALBEDO IN FRACTION
- grid_mapping :
- crs
- long_name :
- Annual maximum snow albedo in fraction
- units :
- 1
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - SNOW(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- SNOW WATER EQUIVALENT
- grid_mapping :
- crs
- long_name :
- Snow water equivalent
- units :
- kg m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SNOWC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- FLAG INDICATING SNOW COVERAGE (1 FOR SNOW COVER)
- grid_mapping :
- crs
- long_name :
- Snow cover fraction
- number_of_significant_digits :
- 5
- units :
- valid_range :
- 0.0, 1.0
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SNOWENERGY(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- energy content in snow relative to 273.16
- grid_mapping :
- crs
- long_name :
- Energy content in snow relative to 273.16 K
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SNOWH(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- PHYSICAL SNOW DEPTH
- grid_mapping :
- crs
- long_name :
- Physical snow depth
- number_of_significant_digits :
- 5
- units :
- m
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SNOW_ACC_NC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED SNOW WATER EQUIVALENT OVER prec_acc_dt PERIODS OF TIME
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated snow water equivalent
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SOILENERGY(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- energy content in soil relative to 273.16
- grid_mapping :
- crs
- long_name :
- Energy content in soil relative to 273.16 K
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SR(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- fraction of frozen precipitation
- grid_mapping :
- crs
- long_name :
- Fraction of frozen precipitation
- number_of_significant_digits :
- 5
- units :
- 1
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - T00()float32...
- description :
- BASE STATE TEMPERATURE
- long_name :
- Base state temperature
- units :
- K
[1 values with dtype=float32]
- T2(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- TEMP at 2 M
- grid_mapping :
- crs
- long_name :
- Temperature at 2 meters
- units :
- K
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - TD2(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- 2-m dewpoint temperature
- grid_mapping :
- crs
- long_name :
- Dewpoint temperature at 2 meters
- units :
- K
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - TISO()float32...
- description :
- TEMP AT WHICH THE BASE T TURNS CONST
- long_name :
- Temperature at which the base perturbation potential temperature turns constant
- units :
- K
[1 values with dtype=float32]
- TK(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- coordinates :
- XLONG XLAT XTIME
- description :
- Air temperature at the lowest model level
- units :
- K
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - TLP()float32...
- description :
- BASE STATE LAPSE RATE
- long_name :
- Base state lapse rate
- units :
[1 values with dtype=float32]
- TLP_STRAT()float32...
- description :
- BASE STATE LAPSE RATE (DT/D(LN(P)) IN STRATOSPHERE
- long_name :
- Base state lapse rate (DT/D(LN(P)) in stratosphere
- units :
- K
[1 values with dtype=float32]
- TRAD(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- surface radiative temperature
- grid_mapping :
- crs
- long_name :
- Surface radiative temperature
- units :
- K
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - TSK(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- SURFACE SKIN TEMPERATURE
- grid_mapping :
- crs
- long_name :
- Surface skin temperature
- units :
- K
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - TSLB(time, soil_layers_stag, y, x)float32dask.array<chunksize=(144, 4, 175, 175), meta=np.ndarray>
- description :
- SOIL TEMPERATURE
- grid_mapping :
- crs
- long_name :
- Soil temperature
- number_of_significant_digits :
- 5
- units :
- K
Array Chunk Bytes 7.43 TiB 67.29 MiB Shape (368064, 4, 1015, 1367) (144, 4, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - TSNO(time, snow_layers_stag, y, x)float32dask.array<chunksize=(144, 3, 175, 175), meta=np.ndarray>
- description :
- snow temperature
- grid_mapping :
- crs
- long_name :
- Snow temperature
- number_of_significant_digits :
- 5
- units :
- K
Array Chunk Bytes 5.57 TiB 50.47 MiB Shape (368064, 3, 1015, 1367) (144, 3, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - TV(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- vegetation leaf temperature
- grid_mapping :
- crs
- long_name :
- Vegetation leaf temperature
- number_of_significant_digits :
- 5
- units :
- K
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - U(time, y, x_stag)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- x-wind component
- grid_mapping :
- crs
- long_name :
- U-component of wind with respect to model grid
- units :
- m s-1
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1368) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - U10(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- U at 10 M
- grid_mapping :
- crs
- long_name :
- U-component of wind at 10 meters with respect to model grid
- units :
- m s-1
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - V(time, y_stag, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- y-wind component
- grid_mapping :
- crs
- long_name :
- V-component of wind with respect to model grid
- units :
- m s-1
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1016, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - V10(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- V at 10 M
- grid_mapping :
- crs
- long_name :
- V-component of wind at 10 meters with respect to model grid
- units :
- m s-1
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - VAR(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- OROGRAPHIC VARIANCE
- grid_mapping :
- crs
- long_name :
- Orographic variance
- units :
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - VAR_SSO(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- variance of subgrid-scale orography
- grid_mapping :
- crs
- long_name :
- Variance of subgrid-scale orography
- units :
- m2
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - XLAND(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LAND MASK (1 FOR LAND, 2 FOR WATER)
- flag_meanings :
- land water
- flag_values :
- 1, 2
- grid_mapping :
- crs
- long_name :
- Land mask (1 for land, 2 for water)
- units :
- valid_range :
- 1.0, 2.0
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - Z(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- coordinates :
- XLONG XLAT XTIME
- description :
- Geopotential Height at the lowest model level (PH+PHB)/9.81
- units :
- meters MSL
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ZETATOP()float32...
- description :
- ZETA AT MODEL TOP
- long_name :
- ZETA at model top
- units :
[1 values with dtype=float32]
- ZNU(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
- eta values on half (mass) levels
- long_name :
- Eta values on half (mass) levels
- units :
Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - ZNW(bottom_top_stag)float32dask.array<chunksize=(51,), meta=np.ndarray>
- description :
- eta values on full (w) levels
- long_name :
- Eta values on full (w) levels
- units :
Array Chunk Bytes 204 B 204 B Shape (51,) (51,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - ZS(soil_layers_stag)float32dask.array<chunksize=(4,), meta=np.ndarray>
- description :
- DEPTHS OF CENTERS OF SOIL LAYERS
- long_name :
- Depths of centers of soil layers
- units :
- m
Array Chunk Bytes 16 B 16 B Shape (4,) (4,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - ZSNSO(time, snso_layers_stag, y, x)float32dask.array<chunksize=(144, 7, 175, 175), meta=np.ndarray>
- description :
- layer-bottom depth from snow surf
- grid_mapping :
- crs
- long_name :
- Layer-bottom depth from snow surface
- number_of_significant_digits :
- 5
- units :
- m
Array Chunk Bytes 13.01 TiB 117.76 MiB Shape (368064, 7, 1015, 1367) (144, 7, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ZWT(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- water table depth
- grid_mapping :
- crs
- long_name :
- Water table depth
- number_of_significant_digits :
- 5
- units :
- m
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - crs()int32...
- grid_mapping_name :
- lambert_conformal_conic
- latitude_of_projection_origin :
- 39.100006103515625
- longitude_of_central_meridian :
- 262.0999984741211
- semi_major_axis :
- 6370000.0
- semi_minor_axis :
- 6370000.0
- standard_parallel :
- [30.0, 50.0]
[1 values with dtype=int32]
- timePandasIndex
PandasIndex(DatetimeIndex(['1979-10-01 00:00:00', '1979-10-01 01:00:00', '1979-10-01 02:00:00', '1979-10-01 03:00:00', '1979-10-01 04:00:00', '1979-10-01 05:00:00', '1979-10-01 06:00:00', '1979-10-01 07:00:00', '1979-10-01 08:00:00', '1979-10-01 09:00:00', ... '2021-09-25 14:00:00', '2021-09-25 15:00:00', '2021-09-25 16:00:00', '2021-09-25 17:00:00', '2021-09-25 18:00:00', '2021-09-25 19:00:00', '2021-09-25 20:00:00', '2021-09-25 21:00:00', '2021-09-25 22:00:00', '2021-09-25 23:00:00'], dtype='datetime64[ns]', name='time', length=368064, freq=None)) - xPandasIndex
PandasIndex(Float64Index([-2732097.901153542, -2728097.901153542, -2724097.901153542, -2720097.901153542, -2716097.901153542, -2712097.901153542, -2708097.901153542, -2704097.901153542, -2700097.901153542, -2696097.901153542, ... 2695902.098846458, 2699902.098846458, 2703902.098846458, 2707902.098846458, 2711902.098846458, 2715902.098846458, 2719902.098846458, 2723902.098846458, 2727902.098846458, 2731902.098846458], dtype='float64', name='x', length=1367)) - yPandasIndex
PandasIndex(Float64Index([-2027960.8996368449, -2023960.8996368449, -2019960.8996368449, -2015960.8996368449, -2011960.8996368449, -2007960.8996368449, -2003960.8996368449, -1999960.8996368449, -1995960.8996368449, -1991960.8996368449, ... 1992039.1003631551, 1996039.1003631551, 2000039.1003631551, 2004039.1003631551, 2008039.1003631551, 2012039.1003631551, 2016039.1003631551, 2020039.1003631551, 2024039.1003631551, 2028039.1003631551], dtype='float64', name='y', length=1015))
- AER_ANGEXP_OPT :
- 1
- AER_ANGEXP_VAL :
- 1.2999999523162842
- AER_AOD550_OPT :
- 1
- AER_AOD550_VAL :
- 0.11999999731779099
- AER_ASY_OPT :
- 1
- AER_ASY_VAL :
- 0.8999999761581421
- AER_OPT :
- 1
- AER_SSA_OPT :
- 1
- AER_SSA_VAL :
- 0.8500000238418579
- AER_TYPE :
- 1
- BLDT :
- 0.0
- BL_PBL_PHYSICS :
- 1
- BOTTOM-TOP_GRID_DIMENSION :
- 51
- BOTTOM-TOP_PATCH_END_STAG :
- 51
- BOTTOM-TOP_PATCH_END_UNSTAG :
- 50
- BOTTOM-TOP_PATCH_START_STAG :
- 1
- BOTTOM-TOP_PATCH_START_UNSTAG :
- 1
- BUCKET_J :
- 1000000000.0
- BUCKET_MM :
- 100.0
- CEN_LAT :
- 39.100006103515625
- CEN_LON :
- -97.89999389648438
- CUDT :
- 5.0
- CU_PHYSICS :
- 0
- Contacts :
- CHANGHAI LIU (chliu@ucar.edu), KYOKO IKEDA (kyoko@ucar.edu)
- DAMPCOEF :
- 0.20000000298023224
- DAMP_OPT :
- 3
- DFI_OPT :
- 0
- DIFF_6TH_FACTOR :
- 0.11999999731779099
- DIFF_6TH_OPT :
- 0
- DIFF_OPT :
- 2
- DT :
- 20.0
- DTRAMP_MIN :
- 60.0
- DVEG :
- 9
- DX :
- 4000.0
- DY :
- 4000.0
- Division :
- NCAR/RAL/HAP
- ETAC :
- 0.0
- FEEDBACK :
- 1
- FGDT :
- 2.0
- FileGenerated :
- 20210204
- GFDDA_END_H :
- 999999
- GFDDA_INTERVAL_M :
- 180
- GMT :
- 0.0
- GPH :
- 4.999999873689376e-05
- GRAV_SETTLING :
- 0
- GRIDTYPE :
- C
- GRID_FDDA :
- 2
- GRID_ID :
- 1
- GRID_SFDDA :
- 0
- GT :
- 4.999999873689376e-05
- GUV :
- 4.999999873689376e-05
- GWD_OPT :
- 0
- HYBRID_OPT :
- -1
- HYPSOMETRIC_OPT :
- 2
- ICLOUD :
- 1
- ICLOUD_CU :
- 0
- IF_RAMPING :
- 1
- ISFFLX :
- 1
- ISFTCFLX :
- 0
- ISHALLOW :
- 0
- ISICE :
- 15
- ISLAKE :
- 21
- ISOILWATER :
- 14
- ISURBAN :
- 13
- ISWATER :
- 17
- I_PARENT_START :
- 1
- JULDAY :
- 274
- JULYR :
- 1979
- J_PARENT_START :
- 1
- KHDIF :
- 0.0
- KM_OPT :
- 4
- KVDIF :
- 0.0
- MAP_PROJ :
- 1
- MAP_PROJ_CHAR :
- Lambert Conformal
- MFSHCONV :
- 0
- MMINLU :
- MODIFIED_IGBP_MODIS_NOAH
- MOAD_CEN_LAT :
- 39.100006103515625
- MOIST_ADV_OPT :
- 1
- MP_PHYSICS :
- 8
- NCO :
- netCDF Operators version 4.9.5 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)
- NUM_LAND_CAT :
- 21
- OBS_NUDGE_OPT :
- 0
- OPT_ALB :
- 2
- OPT_BTR :
- 2
- OPT_CRS :
- 1
- OPT_FRZ :
- 1
- OPT_GLA :
- 1
- OPT_INF :
- 1
- OPT_RAD :
- 3
- OPT_RSF :
- 1
- OPT_RUN :
- 5
- OPT_SFC :
- 1
- OPT_SNF :
- 4
- OPT_STC :
- 3
- OPT_TBOT :
- 1
- PARENT_GRID_RATIO :
- 1
- PARENT_ID :
- 0
- POLE_LAT :
- 90.0
- POLE_LON :
- 0.0
- PREC_ACC_DT :
- 60.0
- Project :
- USGS CONUS404
- RADT :
- 5.0
- RA_LW_PHYSICS :
- 4
- RA_SW_PHYSICS :
- 4
- SCALAR_ADV_OPT :
- 1
- SCALAR_PBLMIX :
- 0
- SF_LAKE_PHYSICS :
- 0
- SF_OCEAN_PHYSICS :
- 0
- SF_SFCLAY_PHYSICS :
- 1
- SF_SURFACE_PHYSICS :
- 4
- SF_URBAN_PHYSICS :
- 0
- SGFDDA_END_H :
- 0
- SGFDDA_INTERVAL_M :
- 0
- SHCU_PHYSICS :
- 0
- SIMULATION_INITIALIZATION_TYPE :
- REAL-DATA CASE
- SIMULATION_START_DATE :
- 1979-10-01_00:00:00
- SKEBS_ON :
- 0
- SMOOTH_OPTION :
- 2
- SOUTH-NORTH_GRID_DIMENSION :
- 1016
- SOUTH-NORTH_PATCH_END_STAG :
- 1016
- SOUTH-NORTH_PATCH_END_UNSTAG :
- 1015
- SOUTH-NORTH_PATCH_START_STAG :
- 1
- SOUTH-NORTH_PATCH_START_UNSTAG :
- 1
- SPEC_BDY_FINAL_MU :
- 1
- SST_UPDATE :
- 1
- STAND_LON :
- -97.9000015258789
- START_DATE :
- 1979-10-01_00:00:00
- SURFACE_INPUT_SOURCE :
- 1
- SWINT_OPT :
- 0
- SWRAD_SCAT :
- 1.0
- Source_Code :
- make_conusii_2d.csh
- TITLE :
- OUTPUT FROM WRF V3.9.1.1 MODEL
- TKE_ADV_OPT :
- 1
- TOPO_WIND :
- 1
- TRACER_PBLMIX :
- 1
- TRUELAT1 :
- 30.0
- TRUELAT2 :
- 50.0
- USE_Q_DIABATIC :
- 0
- USE_THETA_M :
- 0
- WEST-EAST_GRID_DIMENSION :
- 1368
- WEST-EAST_PATCH_END_STAG :
- 1368
- WEST-EAST_PATCH_END_UNSTAG :
- 1367
- WEST-EAST_PATCH_START_STAG :
- 1
- WEST-EAST_PATCH_START_UNSTAG :
- 1
- W_DAMPING :
- 1
- YSU_TOPDOWN_PBLMIX :
- 0
- history :
- Tue Mar 29 16:35:22 2022: ncrcat -A -vW /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_W_d01_1979-10-01_00:00:00.nc /glade/scratch/kyoko/USGS/conus404_production_outputs/OUTPUT/WY1980/wrf2d_d01_1979-10-01_00:00:00 Tue Mar 29 16:35:21 2022: ncrcat -A -vZ /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_Z_d01_1979-10-01_00:00:00.nc /glade/scratch/kyoko/USGS/conus404_production_outputs/OUTPUT/WY1980/wrf2d_d01_1979-10-01_00:00:00
- history_of_appended_files :
- Tue Mar 29 16:35:22 2022: Appended file /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_W_d01_1979-10-01_00:00:00.nc had no "history" attribute Tue Mar 29 16:35:21 2022: Appended file /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_Z_d01_1979-10-01_00:00:00.nc had no "history" attribute
Or you can open the dataset lazily with Dask by setting the parameter chunks={}
# open dataset lazily
# this happens by setting chunks={}
# though you can specify chunks where applicable
c404_hourly_dask = xr.open_zarr(fs_gm, chunks={})
c404_hourly_dask
<xarray.Dataset>
Dimensions: (time: 368064, y: 1015, x: 1367, bottom_top_stag: 51,
bottom_top: 50, soil_layers_stag: 4, x_stag: 1368,
y_stag: 1016, snow_layers_stag: 3, snso_layers_stag: 7)
Coordinates:
lat (y, x) float32 dask.array<chunksize=(175, 175), meta=np.ndarray>
lat_u (y, x_stag) float32 dask.array<chunksize=(175, 175), meta=np.ndarray>
lat_v (y_stag, x) float32 dask.array<chunksize=(175, 175), meta=np.ndarray>
lon (y, x) float32 dask.array<chunksize=(175, 175), meta=np.ndarray>
lon_u (y, x_stag) float32 dask.array<chunksize=(175, 175), meta=np.ndarray>
lon_v (y_stag, x) float32 dask.array<chunksize=(175, 175), meta=np.ndarray>
* time (time) datetime64[ns] 1979-10-01 ... 2021-09-25T23:00:00
* x (x) float64 -2.732e+06 -2.728e+06 ... 2.728e+06 2.732e+06
* y (y) float64 -2.028e+06 -2.024e+06 ... 2.024e+06 2.028e+06
Dimensions without coordinates: bottom_top_stag, bottom_top, soil_layers_stag,
x_stag, y_stag, snow_layers_stag,
snso_layers_stag
Data variables: (12/157)
ACDEWC (time, y, x) float32 dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
ACDRIPR (time, y, x) float32 dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
ACDRIPS (time, y, x) float32 dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
ACECAN (time, y, x) float32 dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
ACEDIR (time, y, x) float32 dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
ACETLSM (time, y, x) float32 dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
... ...
ZNU (bottom_top) float32 dask.array<chunksize=(50,), meta=np.ndarray>
ZNW (bottom_top_stag) float32 dask.array<chunksize=(51,), meta=np.ndarray>
ZS (soil_layers_stag) float32 dask.array<chunksize=(4,), meta=np.ndarray>
ZSNSO (time, snso_layers_stag, y, x) float32 dask.array<chunksize=(144, 7, 175, 175), meta=np.ndarray>
ZWT (time, y, x) float32 dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
crs int32 ...
Attributes: (12/148)
AER_ANGEXP_OPT: 1
AER_ANGEXP_VAL: 1.2999999523162842
AER_AOD550_OPT: 1
AER_AOD550_VAL: 0.11999999731779099
AER_ASY_OPT: 1
AER_ASY_VAL: 0.8999999761581421
... ...
WEST-EAST_PATCH_START_STAG: 1
WEST-EAST_PATCH_START_UNSTAG: 1
W_DAMPING: 1
YSU_TOPDOWN_PBLMIX: 0
history: Tue Mar 29 16:35:22 2022: ncrcat -A -vW ...
history_of_appended_files: Tue Mar 29 16:35:22 2022: Appended file ...- time: 368064
- y: 1015
- x: 1367
- bottom_top_stag: 51
- bottom_top: 50
- soil_layers_stag: 4
- x_stag: 1368
- y_stag: 1016
- snow_layers_stag: 3
- snso_layers_stag: 7
- lat(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LATITUDE, SOUTH IS NEGATIVE
- long_name :
- Latitude, south is negative
- standard_name :
- latitude
- units :
- degree_north
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - lat_u(y, x_stag)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LATITUDE, SOUTH IS NEGATIVE
- long_name :
- Latitude, south is negative
- units :
- degree_north
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1015, 1368) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - lat_v(y_stag, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LATITUDE, SOUTH IS NEGATIVE
- long_name :
- Latitude, south is negative
- units :
- degree_north
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1016, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - lon(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LONGITUDE, WEST IS NEGATIVE
- long_name :
- Longitude, west is negative
- standard_name :
- longitude
- units :
- degree_east
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - lon_u(y, x_stag)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LONGITUDE, WEST IS NEGATIVE
- long_name :
- Longitude, west is negative
- units :
- degree_east
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1015, 1368) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - lon_v(y_stag, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LONGITUDE, WEST IS NEGATIVE
- long_name :
- Longitude, west is negative
- units :
- degree_east
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1016, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - time(time)datetime64[ns]1979-10-01 ... 2021-09-25T23:00:00
- standard_name :
- time
array(['1979-10-01T00:00:00.000000000', '1979-10-01T01:00:00.000000000', '1979-10-01T02:00:00.000000000', ..., '2021-09-25T21:00:00.000000000', '2021-09-25T22:00:00.000000000', '2021-09-25T23:00:00.000000000'], dtype='datetime64[ns]') - x(x)float64-2.732e+06 -2.728e+06 ... 2.732e+06
- long_name :
- x coordinate of projection
- standard_name :
- projection_x_coordinate
- units :
- m
array([-2732097.901154, -2728097.901154, -2724097.901154, ..., 2723902.098846, 2727902.098846, 2731902.098846]) - y(y)float64-2.028e+06 -2.024e+06 ... 2.028e+06
- long_name :
- y coordinate of projection
- standard_name :
- projection_y_coordinate
- units :
- m
array([-2027960.899637, -2023960.899637, -2019960.899637, ..., 2020039.100363, 2024039.100363, 2028039.100363])
- ACDEWC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QDEWC
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy dew rate
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACDRIPR(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QDRIPR
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy precipitation drip rate
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACDRIPS(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QDRIPS
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy snow drip rate
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACECAN(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated ECAN
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated net evaporation of canopy water (evap + sublim - dew - frost)
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACEDIR(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated EDIR
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated net soil evaporation or snowpack sublimation (evap or sublim - dew or frost)
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACETLSM(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated ET
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated total evaporation
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACETRAN(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated ETRAN
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated plant transpiration
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACEVAC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QEVAC
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy evaporation
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACEVB(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated EVB
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated latent heat flux over bare ground
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACEVC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated EVC
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated latent heat flux for canopy layer
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACEVG(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated EVG
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated ground latent heat flux below canopy
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACFROC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QFROC
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy frost
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACFRZC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QFRZC
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated refreezing of canopy liquid water
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACGHB(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated GHB
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated heat flux into soil or snowpack for bare ground
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACGHFLSM(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated total ground heat flux
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated total ground heat flux into soil or snowpack
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACGHV(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated GHV
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated heat flux into soil or snowpack under canopy
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACINTR(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QINTR
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy rain interception rate
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACINTS(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QINTS
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy snow interception rate
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACLHFLSM(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated total latent heat flux
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated total latent heat flux
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACLWDNB(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED DOWNWELLING LONGWAVE FLUX AT BOTTOM
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated downwelling longwave radiation flux at bottom
- units :
- J m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACLWUPB(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED UPWELLING LONGWAVE FLUX AT BOTTOM
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated upwelling longwave radiation flux at bottom
- units :
- J m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACMELTC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QMELTC
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy snow melt
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACPONDING(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated PONDING
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated surface ponding from complete pack melt
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACQLAT(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- accumulated lateral flow
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated groundwater lateral flow
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACQRF(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- accumulated baseflow
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated groundwater baseflow
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACRAINLSM(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated RAINLSM
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated liquid precipitation into land surface model
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACRAINSNOW(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated rain on snow pack
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated rain on snow pack
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACRUNSB(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated RUNSB
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated subsurface runoff
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACRUNSF(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated RUNSF
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated surface runoff
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSHFLSM(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated total sensible heat flux
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated total sensible heat flux
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSNBOT(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QSNBOT
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated liquid water flux out of bottom of snowpack
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSNFRO(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QSNFRO
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated snowpack frost
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSNOM(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED MELTED SNOW
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated total liquid water out of the snowpack
- units :
- kg m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSNOWLSM(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated SNOWLSM
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated frozen precipitation into land surface model
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSNSUB(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QSNSUB
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated snowpack sublimation
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSUBC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QSUBC
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy snow sublimation
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSWDNB(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED DOWNWELLING SHORTWAVE FLUX AT BOTTOM
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated downwelling shortwave radiation flux at bottom
- units :
- J m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSWDNLSM(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated SWDN
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated shortwave radiation down at land surface model
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSWDNT(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED DOWNWELLING SHORTWAVE FLUX AT TOP
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated downwelling shortwave radiation flux at top
- units :
- J m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSWUPB(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED UPWELLING SHORTWAVE FLUX AT BOTTOM
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated upwelling shortwave radiation flux at bottom
- units :
- J m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACSWUPLSM(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated SWUP
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated shortwave radiation up at land surface model
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACTHROR(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QTHROR
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy rain throughfall
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACTHROS(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated QTHROS
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated canopy snow throughfall
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ACTR(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- acccumlated TR
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated transpiration
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ALBEDO(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ALBEDO
- grid_mapping :
- crs
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Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - BF(bottom_top_stag)float32dask.array<chunksize=(51,), meta=np.ndarray>
- description :
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Array Chunk Bytes 204 B 204 B Shape (51,) (51,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - BH(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
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- Half levels, bh=0 => isobaric; bh=znu => sigma
- units :
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Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - C1F(bottom_top_stag)float32dask.array<chunksize=(51,), meta=np.ndarray>
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- units :
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Array Chunk Bytes 204 B 204 B Shape (51,) (51,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - C1H(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
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- Half levels, c1h = d bf / d eta, using znw
- units :
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Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - C2F(bottom_top_stag)float32dask.array<chunksize=(51,), meta=np.ndarray>
- description :
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- units :
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Array Chunk Bytes 204 B 204 B Shape (51,) (51,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - C2H(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
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- Half levels, c2h = (1-c1h)*(p0-pt)
- units :
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Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - C3F(bottom_top_stag)float32dask.array<chunksize=(51,), meta=np.ndarray>
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- units :
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Array Chunk Bytes 204 B 204 B Shape (51,) (51,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - C3H(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
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- units :
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Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - C4F(bottom_top_stag)float32dask.array<chunksize=(51,), meta=np.ndarray>
- description :
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- long_name :
- Full levels, c4f = (eta-bf)*(p0-pt)+pt, using znw
- units :
- Pa
Array Chunk Bytes 204 B 204 B Shape (51,) (51,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - C4H(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
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- long_name :
- Half levels, c4h = (eta-bh)*(p0-pt)+pt, using znu
- units :
- Pa
Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - CANWAT(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
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Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - CF1()float32...
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- 2nd order extrapolation constant
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- CF2()float32...
- description :
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- long_name :
- 2nd order extrapolation constant
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- long_name :
- 2nd order extrapolation constant
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- CFN1()float32...
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- Extrapolation constant
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Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - E2(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
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Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - F(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
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Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - FNM(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
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- ACCUMULATED GRAUPEL WATER EQUIVALENT OVER prec_acc_dt PERIODS OF TIME
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- crs
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- Accumulated graupel water equivalent
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Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - HGT(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
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- crs
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Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - ISLTYP(y, x)int32dask.array<chunksize=(175, 175), meta=np.ndarray>
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- units :
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Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type int32 numpy.ndarray - IVGTYP(y, x)int32dask.array<chunksize=(175, 175), meta=np.ndarray>
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- grid_mapping :
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Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type int32 numpy.ndarray - LAKEMASK(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
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- Lake mask (1 for lake, 0 for non-lake)
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Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - LANDMASK(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
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- grid_mapping :
- crs
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- Land mask (1 for land, 0 for water)
- units :
- valid_range :
- 0.0, 1.0
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - LU_INDEX(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
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- Land use category
- units :
- valid_range :
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Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAPFAC_M(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
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- units :
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAPFAC_MX(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
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- grid_mapping :
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- units :
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAPFAC_MY(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
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- grid_mapping :
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- Map scale factor on mass grid, y direction
- units :
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAPFAC_U(y, x_stag)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
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- units :
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1015, 1368) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAPFAC_UX(y, x_stag)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
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- grid_mapping :
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- Map scale factor on u-grid, x direction
- units :
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1015, 1368) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAPFAC_UY(y, x_stag)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
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- grid_mapping :
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- Map scale factor on u-grid, y direction
- units :
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1015, 1368) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAPFAC_V(y_stag, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
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- grid_mapping :
- crs
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- Map scale factor on v-grid
- units :
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1016, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAPFAC_VX(y_stag, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
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- grid_mapping :
- crs
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- Map scale factor on v-grid, x direction
- units :
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1016, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAPFAC_VY(y_stag, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- Map scale factor on v-grid, y direction
- grid_mapping :
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- Map scale factor on v-grid, y direction
- units :
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1016, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MAX_MSTFX()float32...
- description :
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- long_name :
- Maximum map factor in domain
- units :
[1 values with dtype=float32]
- MAX_MSTFY()float32...
- description :
- Max map factor in domain
- long_name :
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- units :
[1 values with dtype=float32]
- MF_VX_INV(y_stag, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
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- grid_mapping :
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- long_name :
- Inverse map scale factor on v-grid, x direction
- units :
Array Chunk Bytes 5.30 MiB 119.63 kiB Shape (1016, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - MLCAPE(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
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- crs
- long_name :
- Mixed-layer convective available potential energy (CAPE)
- units :
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Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - MUB(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- base state dry air mass in column
- grid_mapping :
- crs
- long_name :
- Base state dry air mass in column
- units :
- Pa
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - P00()float32...
- description :
- BASE STATE PRESURE
- long_name :
- Base state pressure
- units :
- Pa
[1 values with dtype=float32]
- PB(bottom_top, y, x)float32dask.array<chunksize=(50, 175, 175), meta=np.ndarray>
- description :
- BASE STATE PRESSURE
- grid_mapping :
- crs
- long_name :
- Base state pressure
- units :
- Pa
Array Chunk Bytes 264.65 MiB 5.84 MiB Shape (50, 1015, 1367) (50, 175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - PBLH(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- PBL HEIGHT
- grid_mapping :
- crs
- long_name :
- Planetary boundary layer height
- units :
- m
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - PHB(bottom_top_stag, y, x)float32dask.array<chunksize=(51, 175, 175), meta=np.ndarray>
- description :
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- grid_mapping :
- crs
- long_name :
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- units :
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Array Chunk Bytes 269.94 MiB 5.96 MiB Shape (51, 1015, 1367) (51, 175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - PREC_ACC_NC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED GRID SCALE PRECIPITATION OVER prec_acc_dt PERIODS OF TIME
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated grid scale precipitation
- units :
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Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - PSFC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- SFC PRESSURE
- grid_mapping :
- crs
- long_name :
- Surface pressure
- units :
- Pa
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - PWAT(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- coordinates :
- XLONG XLAT XTIME
- description :
- Precipitable Water
- sum_op_ncl :
- dim_sum_n over dimension(s): bottom_top
- units :
- meters
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - P_STRAT()float32...
- description :
- BASE STATE PRESSURE AT BOTTOM OF STRATOSPHERE
- long_name :
- Base state pressure at bottom of stratosphere
- units :
- Pa
[1 values with dtype=float32]
- P_TOP()float32...
- description :
- PRESSURE TOP OF THE MODEL
- long_name :
- Pressure top of the model
- units :
- Pa
[1 values with dtype=float32]
- Q2(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- QV at 2 M
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- crs
- long_name :
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- units :
- kg kg-1
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - QRFS(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- sum baseflow
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated baseflow
- units :
- m
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - QSLAT(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- sum lateral flow
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated groundwater lateral flow
- units :
- m
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - QSPRINGS(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- sum seeping water
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated seeping water
- units :
- m
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - QVAPOR(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- Water vapor mixing ratio
- grid_mapping :
- crs
- long_name :
- Water vapor mixing ratio
- units :
- kg kg-1
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - RDN(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
- inverse d(eta) values between half (mass) levels
- long_name :
- Inverse d(eta) values between half (mass) levels
- units :
Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - RDNW(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
- inverse d(eta) values between full (w) levels
- long_name :
- Inverse d(eta) values between full (w) levels
- units :
Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - RDX()float32...
- description :
- INVERSE X GRID LENGTH
- long_name :
- Inverse X grid length
- units :
- m-1
[1 values with dtype=float32]
- RDY()float32...
- description :
- INVERSE Y GRID LENGTH
- long_name :
- Inverse Y grid length
- units :
- m-1
[1 values with dtype=float32]
- RECH(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- sum water table recharge
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated water table recharge
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - RH2(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- grid_mapping :
- crs
- long_name :
- Relative humidity at 2 meters
- notes :
- Tetens equation
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SH2(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- grid_mapping :
- crs
- long_name :
- Specific humidity at 2 meters
- units :
- kg kg-1
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SH2O(time, soil_layers_stag, y, x)float32dask.array<chunksize=(144, 4, 175, 175), meta=np.ndarray>
- description :
- SOIL LIQUID WATER
- grid_mapping :
- crs
- long_name :
- Soil liquid water
- number_of_significant_digits :
- 5
- units :
- m3 m-3
Array Chunk Bytes 7.43 TiB 67.29 MiB Shape (368064, 4, 1015, 1367) (144, 4, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SHDMAX(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- ANNUAL MAX VEG FRACTION
- grid_mapping :
- crs
- long_name :
- Annual maximum vegetation fraction
- units :
- 1
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - SHDMIN(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- ANNUAL MIN VEG FRACTION
- grid_mapping :
- crs
- long_name :
- Annual minimum vegetation fraction
- units :
- 1
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - SINALPHA(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- Local sine of map rotation
- grid_mapping :
- crs
- long_name :
- Local sine of map rotation
- units :
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - SMCWTD(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- deep soil moisture
- grid_mapping :
- crs
- long_name :
- Deep soil moisture
- units :
- m3 m-3
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SMOIS(time, soil_layers_stag, y, x)float32dask.array<chunksize=(144, 4, 175, 175), meta=np.ndarray>
- description :
- SOIL MOISTURE
- grid_mapping :
- crs
- long_name :
- Soil moisture
- number_of_significant_digits :
- 5
- units :
- m3 m-3
Array Chunk Bytes 7.43 TiB 67.29 MiB Shape (368064, 4, 1015, 1367) (144, 4, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SNICE(time, snow_layers_stag, y, x)float32dask.array<chunksize=(144, 3, 175, 175), meta=np.ndarray>
- description :
- snow layer ice
- grid_mapping :
- crs
- long_name :
- Snow layer ice
- units :
- mm
Array Chunk Bytes 5.57 TiB 50.47 MiB Shape (368064, 3, 1015, 1367) (144, 3, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SNLIQ(time, snow_layers_stag, y, x)float32dask.array<chunksize=(144, 3, 175, 175), meta=np.ndarray>
- description :
- snow layer liquid
- grid_mapping :
- crs
- long_name :
- Snow layer liquid
- units :
- mm
Array Chunk Bytes 5.57 TiB 50.47 MiB Shape (368064, 3, 1015, 1367) (144, 3, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SNOALB(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- ANNUAL MAX SNOW ALBEDO IN FRACTION
- grid_mapping :
- crs
- long_name :
- Annual maximum snow albedo in fraction
- units :
- 1
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - SNOW(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- SNOW WATER EQUIVALENT
- grid_mapping :
- crs
- long_name :
- Snow water equivalent
- units :
- kg m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SNOWC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- FLAG INDICATING SNOW COVERAGE (1 FOR SNOW COVER)
- grid_mapping :
- crs
- long_name :
- Snow cover fraction
- number_of_significant_digits :
- 5
- units :
- valid_range :
- 0.0, 1.0
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SNOWENERGY(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- energy content in snow relative to 273.16
- grid_mapping :
- crs
- long_name :
- Energy content in snow relative to 273.16 K
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SNOWH(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- PHYSICAL SNOW DEPTH
- grid_mapping :
- crs
- long_name :
- Physical snow depth
- number_of_significant_digits :
- 5
- units :
- m
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SNOW_ACC_NC(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED SNOW WATER EQUIVALENT OVER prec_acc_dt PERIODS OF TIME
- grid_mapping :
- crs
- integration_length :
- accumulated over prior 60 minutes
- long_name :
- Accumulated snow water equivalent
- units :
- mm
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SOILENERGY(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- energy content in soil relative to 273.16
- grid_mapping :
- crs
- long_name :
- Energy content in soil relative to 273.16 K
- units :
- kJ m-2
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - SR(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- fraction of frozen precipitation
- grid_mapping :
- crs
- long_name :
- Fraction of frozen precipitation
- number_of_significant_digits :
- 5
- units :
- 1
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - T00()float32...
- description :
- BASE STATE TEMPERATURE
- long_name :
- Base state temperature
- units :
- K
[1 values with dtype=float32]
- T2(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- TEMP at 2 M
- grid_mapping :
- crs
- long_name :
- Temperature at 2 meters
- units :
- K
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - TD2(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- 2-m dewpoint temperature
- grid_mapping :
- crs
- long_name :
- Dewpoint temperature at 2 meters
- units :
- K
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - TISO()float32...
- description :
- TEMP AT WHICH THE BASE T TURNS CONST
- long_name :
- Temperature at which the base perturbation potential temperature turns constant
- units :
- K
[1 values with dtype=float32]
- TK(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- coordinates :
- XLONG XLAT XTIME
- description :
- Air temperature at the lowest model level
- units :
- K
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - TLP()float32...
- description :
- BASE STATE LAPSE RATE
- long_name :
- Base state lapse rate
- units :
[1 values with dtype=float32]
- TLP_STRAT()float32...
- description :
- BASE STATE LAPSE RATE (DT/D(LN(P)) IN STRATOSPHERE
- long_name :
- Base state lapse rate (DT/D(LN(P)) in stratosphere
- units :
- K
[1 values with dtype=float32]
- TRAD(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- surface radiative temperature
- grid_mapping :
- crs
- long_name :
- Surface radiative temperature
- units :
- K
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - TSK(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- SURFACE SKIN TEMPERATURE
- grid_mapping :
- crs
- long_name :
- Surface skin temperature
- units :
- K
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - TSLB(time, soil_layers_stag, y, x)float32dask.array<chunksize=(144, 4, 175, 175), meta=np.ndarray>
- description :
- SOIL TEMPERATURE
- grid_mapping :
- crs
- long_name :
- Soil temperature
- number_of_significant_digits :
- 5
- units :
- K
Array Chunk Bytes 7.43 TiB 67.29 MiB Shape (368064, 4, 1015, 1367) (144, 4, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - TSNO(time, snow_layers_stag, y, x)float32dask.array<chunksize=(144, 3, 175, 175), meta=np.ndarray>
- description :
- snow temperature
- grid_mapping :
- crs
- long_name :
- Snow temperature
- number_of_significant_digits :
- 5
- units :
- K
Array Chunk Bytes 5.57 TiB 50.47 MiB Shape (368064, 3, 1015, 1367) (144, 3, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - TV(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- vegetation leaf temperature
- grid_mapping :
- crs
- long_name :
- Vegetation leaf temperature
- number_of_significant_digits :
- 5
- units :
- K
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - U(time, y, x_stag)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- x-wind component
- grid_mapping :
- crs
- long_name :
- U-component of wind with respect to model grid
- units :
- m s-1
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1368) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - U10(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- U at 10 M
- grid_mapping :
- crs
- long_name :
- U-component of wind at 10 meters with respect to model grid
- units :
- m s-1
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - V(time, y_stag, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- y-wind component
- grid_mapping :
- crs
- long_name :
- V-component of wind with respect to model grid
- units :
- m s-1
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1016, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - V10(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- V at 10 M
- grid_mapping :
- crs
- long_name :
- V-component of wind at 10 meters with respect to model grid
- units :
- m s-1
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - VAR(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- OROGRAPHIC VARIANCE
- grid_mapping :
- crs
- long_name :
- Orographic variance
- units :
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - VAR_SSO(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- variance of subgrid-scale orography
- grid_mapping :
- crs
- long_name :
- Variance of subgrid-scale orography
- units :
- m2
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - XLAND(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LAND MASK (1 FOR LAND, 2 FOR WATER)
- flag_meanings :
- land water
- flag_values :
- 1, 2
- grid_mapping :
- crs
- long_name :
- Land mask (1 for land, 2 for water)
- units :
- valid_range :
- 1.0, 2.0
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - Z(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- coordinates :
- XLONG XLAT XTIME
- description :
- Geopotential Height at the lowest model level (PH+PHB)/9.81
- units :
- meters MSL
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ZETATOP()float32...
- description :
- ZETA AT MODEL TOP
- long_name :
- ZETA at model top
- units :
[1 values with dtype=float32]
- ZNU(bottom_top)float32dask.array<chunksize=(50,), meta=np.ndarray>
- description :
- eta values on half (mass) levels
- long_name :
- Eta values on half (mass) levels
- units :
Array Chunk Bytes 200 B 200 B Shape (50,) (50,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - ZNW(bottom_top_stag)float32dask.array<chunksize=(51,), meta=np.ndarray>
- description :
- eta values on full (w) levels
- long_name :
- Eta values on full (w) levels
- units :
Array Chunk Bytes 204 B 204 B Shape (51,) (51,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - ZS(soil_layers_stag)float32dask.array<chunksize=(4,), meta=np.ndarray>
- description :
- DEPTHS OF CENTERS OF SOIL LAYERS
- long_name :
- Depths of centers of soil layers
- units :
- m
Array Chunk Bytes 16 B 16 B Shape (4,) (4,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray - ZSNSO(time, snso_layers_stag, y, x)float32dask.array<chunksize=(144, 7, 175, 175), meta=np.ndarray>
- description :
- layer-bottom depth from snow surf
- grid_mapping :
- crs
- long_name :
- Layer-bottom depth from snow surface
- number_of_significant_digits :
- 5
- units :
- m
Array Chunk Bytes 13.01 TiB 117.76 MiB Shape (368064, 7, 1015, 1367) (144, 7, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - ZWT(time, y, x)float32dask.array<chunksize=(144, 175, 175), meta=np.ndarray>
- description :
- water table depth
- grid_mapping :
- crs
- long_name :
- Water table depth
- number_of_significant_digits :
- 5
- units :
- m
Array Chunk Bytes 1.86 TiB 16.82 MiB Shape (368064, 1015, 1367) (144, 175, 175) Dask graph 122688 chunks in 2 graph layers Data type float32 numpy.ndarray - crs()int32...
- grid_mapping_name :
- lambert_conformal_conic
- latitude_of_projection_origin :
- 39.100006103515625
- longitude_of_central_meridian :
- 262.0999984741211
- semi_major_axis :
- 6370000.0
- semi_minor_axis :
- 6370000.0
- standard_parallel :
- [30.0, 50.0]
[1 values with dtype=int32]
- timePandasIndex
PandasIndex(DatetimeIndex(['1979-10-01 00:00:00', '1979-10-01 01:00:00', '1979-10-01 02:00:00', '1979-10-01 03:00:00', '1979-10-01 04:00:00', '1979-10-01 05:00:00', '1979-10-01 06:00:00', '1979-10-01 07:00:00', '1979-10-01 08:00:00', '1979-10-01 09:00:00', ... '2021-09-25 14:00:00', '2021-09-25 15:00:00', '2021-09-25 16:00:00', '2021-09-25 17:00:00', '2021-09-25 18:00:00', '2021-09-25 19:00:00', '2021-09-25 20:00:00', '2021-09-25 21:00:00', '2021-09-25 22:00:00', '2021-09-25 23:00:00'], dtype='datetime64[ns]', name='time', length=368064, freq=None)) - xPandasIndex
PandasIndex(Float64Index([-2732097.901153542, -2728097.901153542, -2724097.901153542, -2720097.901153542, -2716097.901153542, -2712097.901153542, -2708097.901153542, -2704097.901153542, -2700097.901153542, -2696097.901153542, ... 2695902.098846458, 2699902.098846458, 2703902.098846458, 2707902.098846458, 2711902.098846458, 2715902.098846458, 2719902.098846458, 2723902.098846458, 2727902.098846458, 2731902.098846458], dtype='float64', name='x', length=1367)) - yPandasIndex
PandasIndex(Float64Index([-2027960.8996368449, -2023960.8996368449, -2019960.8996368449, -2015960.8996368449, -2011960.8996368449, -2007960.8996368449, -2003960.8996368449, -1999960.8996368449, -1995960.8996368449, -1991960.8996368449, ... 1992039.1003631551, 1996039.1003631551, 2000039.1003631551, 2004039.1003631551, 2008039.1003631551, 2012039.1003631551, 2016039.1003631551, 2020039.1003631551, 2024039.1003631551, 2028039.1003631551], dtype='float64', name='y', length=1015))
- AER_ANGEXP_OPT :
- 1
- AER_ANGEXP_VAL :
- 1.2999999523162842
- AER_AOD550_OPT :
- 1
- AER_AOD550_VAL :
- 0.11999999731779099
- AER_ASY_OPT :
- 1
- AER_ASY_VAL :
- 0.8999999761581421
- AER_OPT :
- 1
- AER_SSA_OPT :
- 1
- AER_SSA_VAL :
- 0.8500000238418579
- AER_TYPE :
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- BLDT :
- 0.0
- BL_PBL_PHYSICS :
- 1
- BOTTOM-TOP_GRID_DIMENSION :
- 51
- BOTTOM-TOP_PATCH_END_STAG :
- 51
- BOTTOM-TOP_PATCH_END_UNSTAG :
- 50
- BOTTOM-TOP_PATCH_START_STAG :
- 1
- BOTTOM-TOP_PATCH_START_UNSTAG :
- 1
- BUCKET_J :
- 1000000000.0
- BUCKET_MM :
- 100.0
- CEN_LAT :
- 39.100006103515625
- CEN_LON :
- -97.89999389648438
- CUDT :
- 5.0
- CU_PHYSICS :
- 0
- Contacts :
- CHANGHAI LIU (chliu@ucar.edu), KYOKO IKEDA (kyoko@ucar.edu)
- DAMPCOEF :
- 0.20000000298023224
- DAMP_OPT :
- 3
- DFI_OPT :
- 0
- DIFF_6TH_FACTOR :
- 0.11999999731779099
- DIFF_6TH_OPT :
- 0
- DIFF_OPT :
- 2
- DT :
- 20.0
- DTRAMP_MIN :
- 60.0
- DVEG :
- 9
- DX :
- 4000.0
- DY :
- 4000.0
- Division :
- NCAR/RAL/HAP
- ETAC :
- 0.0
- FEEDBACK :
- 1
- FGDT :
- 2.0
- FileGenerated :
- 20210204
- GFDDA_END_H :
- 999999
- GFDDA_INTERVAL_M :
- 180
- GMT :
- 0.0
- GPH :
- 4.999999873689376e-05
- GRAV_SETTLING :
- 0
- GRIDTYPE :
- C
- GRID_FDDA :
- 2
- GRID_ID :
- 1
- GRID_SFDDA :
- 0
- GT :
- 4.999999873689376e-05
- GUV :
- 4.999999873689376e-05
- GWD_OPT :
- 0
- HYBRID_OPT :
- -1
- HYPSOMETRIC_OPT :
- 2
- ICLOUD :
- 1
- ICLOUD_CU :
- 0
- IF_RAMPING :
- 1
- ISFFLX :
- 1
- ISFTCFLX :
- 0
- ISHALLOW :
- 0
- ISICE :
- 15
- ISLAKE :
- 21
- ISOILWATER :
- 14
- ISURBAN :
- 13
- ISWATER :
- 17
- I_PARENT_START :
- 1
- JULDAY :
- 274
- JULYR :
- 1979
- J_PARENT_START :
- 1
- KHDIF :
- 0.0
- KM_OPT :
- 4
- KVDIF :
- 0.0
- MAP_PROJ :
- 1
- MAP_PROJ_CHAR :
- Lambert Conformal
- MFSHCONV :
- 0
- MMINLU :
- MODIFIED_IGBP_MODIS_NOAH
- MOAD_CEN_LAT :
- 39.100006103515625
- MOIST_ADV_OPT :
- 1
- MP_PHYSICS :
- 8
- NCO :
- netCDF Operators version 4.9.5 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)
- NUM_LAND_CAT :
- 21
- OBS_NUDGE_OPT :
- 0
- OPT_ALB :
- 2
- OPT_BTR :
- 2
- OPT_CRS :
- 1
- OPT_FRZ :
- 1
- OPT_GLA :
- 1
- OPT_INF :
- 1
- OPT_RAD :
- 3
- OPT_RSF :
- 1
- OPT_RUN :
- 5
- OPT_SFC :
- 1
- OPT_SNF :
- 4
- OPT_STC :
- 3
- OPT_TBOT :
- 1
- PARENT_GRID_RATIO :
- 1
- PARENT_ID :
- 0
- POLE_LAT :
- 90.0
- POLE_LON :
- 0.0
- PREC_ACC_DT :
- 60.0
- Project :
- USGS CONUS404
- RADT :
- 5.0
- RA_LW_PHYSICS :
- 4
- RA_SW_PHYSICS :
- 4
- SCALAR_ADV_OPT :
- 1
- SCALAR_PBLMIX :
- 0
- SF_LAKE_PHYSICS :
- 0
- SF_OCEAN_PHYSICS :
- 0
- SF_SFCLAY_PHYSICS :
- 1
- SF_SURFACE_PHYSICS :
- 4
- SF_URBAN_PHYSICS :
- 0
- SGFDDA_END_H :
- 0
- SGFDDA_INTERVAL_M :
- 0
- SHCU_PHYSICS :
- 0
- SIMULATION_INITIALIZATION_TYPE :
- REAL-DATA CASE
- SIMULATION_START_DATE :
- 1979-10-01_00:00:00
- SKEBS_ON :
- 0
- SMOOTH_OPTION :
- 2
- SOUTH-NORTH_GRID_DIMENSION :
- 1016
- SOUTH-NORTH_PATCH_END_STAG :
- 1016
- SOUTH-NORTH_PATCH_END_UNSTAG :
- 1015
- SOUTH-NORTH_PATCH_START_STAG :
- 1
- SOUTH-NORTH_PATCH_START_UNSTAG :
- 1
- SPEC_BDY_FINAL_MU :
- 1
- SST_UPDATE :
- 1
- STAND_LON :
- -97.9000015258789
- START_DATE :
- 1979-10-01_00:00:00
- SURFACE_INPUT_SOURCE :
- 1
- SWINT_OPT :
- 0
- SWRAD_SCAT :
- 1.0
- Source_Code :
- make_conusii_2d.csh
- TITLE :
- OUTPUT FROM WRF V3.9.1.1 MODEL
- TKE_ADV_OPT :
- 1
- TOPO_WIND :
- 1
- TRACER_PBLMIX :
- 1
- TRUELAT1 :
- 30.0
- TRUELAT2 :
- 50.0
- USE_Q_DIABATIC :
- 0
- USE_THETA_M :
- 0
- WEST-EAST_GRID_DIMENSION :
- 1368
- WEST-EAST_PATCH_END_STAG :
- 1368
- WEST-EAST_PATCH_END_UNSTAG :
- 1367
- WEST-EAST_PATCH_START_STAG :
- 1
- WEST-EAST_PATCH_START_UNSTAG :
- 1
- W_DAMPING :
- 1
- YSU_TOPDOWN_PBLMIX :
- 0
- history :
- Tue Mar 29 16:35:22 2022: ncrcat -A -vW /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_W_d01_1979-10-01_00:00:00.nc /glade/scratch/kyoko/USGS/conus404_production_outputs/OUTPUT/WY1980/wrf2d_d01_1979-10-01_00:00:00 Tue Mar 29 16:35:21 2022: ncrcat -A -vZ /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_Z_d01_1979-10-01_00:00:00.nc /glade/scratch/kyoko/USGS/conus404_production_outputs/OUTPUT/WY1980/wrf2d_d01_1979-10-01_00:00:00
- history_of_appended_files :
- Tue Mar 29 16:35:22 2022: Appended file /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_W_d01_1979-10-01_00:00:00.nc had no "history" attribute Tue Mar 29 16:35:21 2022: Appended file /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_Z_d01_1979-10-01_00:00:00.nc had no "history" attribute
The datasets look the same but the second takes up less space in memory. Now to show an example of lazy computation using radiation variables to calculate a made up variable, FAKERAD. Here are the equations: \begin{equation}FAKERAD = DNB - UPB\end{equation} \begin{equation}DNB = ACSWDNB - ACLWDNB\end{equation} \begin{equation}UPB = ACSWUPB - ACLWUPB\end{equation}
c404_hourly_dask_sub = c404_hourly_dask[["ACSWDNB", "ACSWUPB", "ACLWDNB", "ACLWUPB"]]
c404_hourly_dask_sub = c404_hourly_dask_sub.sel(time=slice('1979-10-01T00:00:00.000000000', '1979-10-01T00:00:00.000000000')) #subset to single time step
c404_hourly_dask_sub
<xarray.Dataset>
Dimensions: (time: 1, y: 1015, x: 1367)
Coordinates:
lat (y, x) float32 dask.array<chunksize=(175, 175), meta=np.ndarray>
lon (y, x) float32 dask.array<chunksize=(175, 175), meta=np.ndarray>
* time (time) datetime64[ns] 1979-10-01
* x (x) float64 -2.732e+06 -2.728e+06 ... 2.728e+06 2.732e+06
* y (y) float64 -2.028e+06 -2.024e+06 -2.02e+06 ... 2.024e+06 2.028e+06
Data variables:
ACSWDNB (time, y, x) float32 dask.array<chunksize=(1, 175, 175), meta=np.ndarray>
ACSWUPB (time, y, x) float32 dask.array<chunksize=(1, 175, 175), meta=np.ndarray>
ACLWDNB (time, y, x) float32 dask.array<chunksize=(1, 175, 175), meta=np.ndarray>
ACLWUPB (time, y, x) float32 dask.array<chunksize=(1, 175, 175), meta=np.ndarray>
Attributes: (12/148)
AER_ANGEXP_OPT: 1
AER_ANGEXP_VAL: 1.2999999523162842
AER_AOD550_OPT: 1
AER_AOD550_VAL: 0.11999999731779099
AER_ASY_OPT: 1
AER_ASY_VAL: 0.8999999761581421
... ...
WEST-EAST_PATCH_START_STAG: 1
WEST-EAST_PATCH_START_UNSTAG: 1
W_DAMPING: 1
YSU_TOPDOWN_PBLMIX: 0
history: Tue Mar 29 16:35:22 2022: ncrcat -A -vW ...
history_of_appended_files: Tue Mar 29 16:35:22 2022: Appended file ...- time: 1
- y: 1015
- x: 1367
- lat(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LATITUDE, SOUTH IS NEGATIVE
- long_name :
- Latitude, south is negative
- standard_name :
- latitude
- units :
- degree_north
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - lon(y, x)float32dask.array<chunksize=(175, 175), meta=np.ndarray>
- description :
- LONGITUDE, WEST IS NEGATIVE
- long_name :
- Longitude, west is negative
- standard_name :
- longitude
- units :
- degree_east
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1015, 1367) (175, 175) Dask graph 48 chunks in 2 graph layers Data type float32 numpy.ndarray - time(time)datetime64[ns]1979-10-01
- standard_name :
- time
array(['1979-10-01T00:00:00.000000000'], dtype='datetime64[ns]')
- x(x)float64-2.732e+06 -2.728e+06 ... 2.732e+06
- long_name :
- x coordinate of projection
- standard_name :
- projection_x_coordinate
- units :
- m
array([-2732097.901154, -2728097.901154, -2724097.901154, ..., 2723902.098846, 2727902.098846, 2731902.098846]) - y(y)float64-2.028e+06 -2.024e+06 ... 2.028e+06
- long_name :
- y coordinate of projection
- standard_name :
- projection_y_coordinate
- units :
- m
array([-2027960.899637, -2023960.899637, -2019960.899637, ..., 2020039.100363, 2024039.100363, 2028039.100363])
- ACSWDNB(time, y, x)float32dask.array<chunksize=(1, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED DOWNWELLING SHORTWAVE FLUX AT BOTTOM
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated downwelling shortwave radiation flux at bottom
- units :
- J m-2
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1, 1015, 1367) (1, 175, 175) Dask graph 48 chunks in 3 graph layers Data type float32 numpy.ndarray - ACSWUPB(time, y, x)float32dask.array<chunksize=(1, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED UPWELLING SHORTWAVE FLUX AT BOTTOM
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated upwelling shortwave radiation flux at bottom
- units :
- J m-2
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1, 1015, 1367) (1, 175, 175) Dask graph 48 chunks in 3 graph layers Data type float32 numpy.ndarray - ACLWDNB(time, y, x)float32dask.array<chunksize=(1, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED DOWNWELLING LONGWAVE FLUX AT BOTTOM
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated downwelling longwave radiation flux at bottom
- units :
- J m-2
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1, 1015, 1367) (1, 175, 175) Dask graph 48 chunks in 3 graph layers Data type float32 numpy.ndarray - ACLWUPB(time, y, x)float32dask.array<chunksize=(1, 175, 175), meta=np.ndarray>
- description :
- ACCUMULATED UPWELLING LONGWAVE FLUX AT BOTTOM
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated upwelling longwave radiation flux at bottom
- units :
- J m-2
Array Chunk Bytes 5.29 MiB 119.63 kiB Shape (1, 1015, 1367) (1, 175, 175) Dask graph 48 chunks in 3 graph layers Data type float32 numpy.ndarray
- timePandasIndex
PandasIndex(DatetimeIndex(['1979-10-01'], dtype='datetime64[ns]', name='time', freq=None))
- xPandasIndex
PandasIndex(Float64Index([-2732097.901153542, -2728097.901153542, -2724097.901153542, -2720097.901153542, -2716097.901153542, -2712097.901153542, -2708097.901153542, -2704097.901153542, -2700097.901153542, -2696097.901153542, ... 2695902.098846458, 2699902.098846458, 2703902.098846458, 2707902.098846458, 2711902.098846458, 2715902.098846458, 2719902.098846458, 2723902.098846458, 2727902.098846458, 2731902.098846458], dtype='float64', name='x', length=1367)) - yPandasIndex
PandasIndex(Float64Index([-2027960.8996368449, -2023960.8996368449, -2019960.8996368449, -2015960.8996368449, -2011960.8996368449, -2007960.8996368449, -2003960.8996368449, -1999960.8996368449, -1995960.8996368449, -1991960.8996368449, ... 1992039.1003631551, 1996039.1003631551, 2000039.1003631551, 2004039.1003631551, 2008039.1003631551, 2012039.1003631551, 2016039.1003631551, 2020039.1003631551, 2024039.1003631551, 2028039.1003631551], dtype='float64', name='y', length=1015))
- AER_ANGEXP_OPT :
- 1
- AER_ANGEXP_VAL :
- 1.2999999523162842
- AER_AOD550_OPT :
- 1
- AER_AOD550_VAL :
- 0.11999999731779099
- AER_ASY_OPT :
- 1
- AER_ASY_VAL :
- 0.8999999761581421
- AER_OPT :
- 1
- AER_SSA_OPT :
- 1
- AER_SSA_VAL :
- 0.8500000238418579
- AER_TYPE :
- 1
- BLDT :
- 0.0
- BL_PBL_PHYSICS :
- 1
- BOTTOM-TOP_GRID_DIMENSION :
- 51
- BOTTOM-TOP_PATCH_END_STAG :
- 51
- BOTTOM-TOP_PATCH_END_UNSTAG :
- 50
- BOTTOM-TOP_PATCH_START_STAG :
- 1
- BOTTOM-TOP_PATCH_START_UNSTAG :
- 1
- BUCKET_J :
- 1000000000.0
- BUCKET_MM :
- 100.0
- CEN_LAT :
- 39.100006103515625
- CEN_LON :
- -97.89999389648438
- CUDT :
- 5.0
- CU_PHYSICS :
- 0
- Contacts :
- CHANGHAI LIU (chliu@ucar.edu), KYOKO IKEDA (kyoko@ucar.edu)
- DAMPCOEF :
- 0.20000000298023224
- DAMP_OPT :
- 3
- DFI_OPT :
- 0
- DIFF_6TH_FACTOR :
- 0.11999999731779099
- DIFF_6TH_OPT :
- 0
- DIFF_OPT :
- 2
- DT :
- 20.0
- DTRAMP_MIN :
- 60.0
- DVEG :
- 9
- DX :
- 4000.0
- DY :
- 4000.0
- Division :
- NCAR/RAL/HAP
- ETAC :
- 0.0
- FEEDBACK :
- 1
- FGDT :
- 2.0
- FileGenerated :
- 20210204
- GFDDA_END_H :
- 999999
- GFDDA_INTERVAL_M :
- 180
- GMT :
- 0.0
- GPH :
- 4.999999873689376e-05
- GRAV_SETTLING :
- 0
- GRIDTYPE :
- C
- GRID_FDDA :
- 2
- GRID_ID :
- 1
- GRID_SFDDA :
- 0
- GT :
- 4.999999873689376e-05
- GUV :
- 4.999999873689376e-05
- GWD_OPT :
- 0
- HYBRID_OPT :
- -1
- HYPSOMETRIC_OPT :
- 2
- ICLOUD :
- 1
- ICLOUD_CU :
- 0
- IF_RAMPING :
- 1
- ISFFLX :
- 1
- ISFTCFLX :
- 0
- ISHALLOW :
- 0
- ISICE :
- 15
- ISLAKE :
- 21
- ISOILWATER :
- 14
- ISURBAN :
- 13
- ISWATER :
- 17
- I_PARENT_START :
- 1
- JULDAY :
- 274
- JULYR :
- 1979
- J_PARENT_START :
- 1
- KHDIF :
- 0.0
- KM_OPT :
- 4
- KVDIF :
- 0.0
- MAP_PROJ :
- 1
- MAP_PROJ_CHAR :
- Lambert Conformal
- MFSHCONV :
- 0
- MMINLU :
- MODIFIED_IGBP_MODIS_NOAH
- MOAD_CEN_LAT :
- 39.100006103515625
- MOIST_ADV_OPT :
- 1
- MP_PHYSICS :
- 8
- NCO :
- netCDF Operators version 4.9.5 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)
- NUM_LAND_CAT :
- 21
- OBS_NUDGE_OPT :
- 0
- OPT_ALB :
- 2
- OPT_BTR :
- 2
- OPT_CRS :
- 1
- OPT_FRZ :
- 1
- OPT_GLA :
- 1
- OPT_INF :
- 1
- OPT_RAD :
- 3
- OPT_RSF :
- 1
- OPT_RUN :
- 5
- OPT_SFC :
- 1
- OPT_SNF :
- 4
- OPT_STC :
- 3
- OPT_TBOT :
- 1
- PARENT_GRID_RATIO :
- 1
- PARENT_ID :
- 0
- POLE_LAT :
- 90.0
- POLE_LON :
- 0.0
- PREC_ACC_DT :
- 60.0
- Project :
- USGS CONUS404
- RADT :
- 5.0
- RA_LW_PHYSICS :
- 4
- RA_SW_PHYSICS :
- 4
- SCALAR_ADV_OPT :
- 1
- SCALAR_PBLMIX :
- 0
- SF_LAKE_PHYSICS :
- 0
- SF_OCEAN_PHYSICS :
- 0
- SF_SFCLAY_PHYSICS :
- 1
- SF_SURFACE_PHYSICS :
- 4
- SF_URBAN_PHYSICS :
- 0
- SGFDDA_END_H :
- 0
- SGFDDA_INTERVAL_M :
- 0
- SHCU_PHYSICS :
- 0
- SIMULATION_INITIALIZATION_TYPE :
- REAL-DATA CASE
- SIMULATION_START_DATE :
- 1979-10-01_00:00:00
- SKEBS_ON :
- 0
- SMOOTH_OPTION :
- 2
- SOUTH-NORTH_GRID_DIMENSION :
- 1016
- SOUTH-NORTH_PATCH_END_STAG :
- 1016
- SOUTH-NORTH_PATCH_END_UNSTAG :
- 1015
- SOUTH-NORTH_PATCH_START_STAG :
- 1
- SOUTH-NORTH_PATCH_START_UNSTAG :
- 1
- SPEC_BDY_FINAL_MU :
- 1
- SST_UPDATE :
- 1
- STAND_LON :
- -97.9000015258789
- START_DATE :
- 1979-10-01_00:00:00
- SURFACE_INPUT_SOURCE :
- 1
- SWINT_OPT :
- 0
- SWRAD_SCAT :
- 1.0
- Source_Code :
- make_conusii_2d.csh
- TITLE :
- OUTPUT FROM WRF V3.9.1.1 MODEL
- TKE_ADV_OPT :
- 1
- TOPO_WIND :
- 1
- TRACER_PBLMIX :
- 1
- TRUELAT1 :
- 30.0
- TRUELAT2 :
- 50.0
- USE_Q_DIABATIC :
- 0
- USE_THETA_M :
- 0
- WEST-EAST_GRID_DIMENSION :
- 1368
- WEST-EAST_PATCH_END_STAG :
- 1368
- WEST-EAST_PATCH_END_UNSTAG :
- 1367
- WEST-EAST_PATCH_START_STAG :
- 1
- WEST-EAST_PATCH_START_UNSTAG :
- 1
- W_DAMPING :
- 1
- YSU_TOPDOWN_PBLMIX :
- 0
- history :
- Tue Mar 29 16:35:22 2022: ncrcat -A -vW /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_W_d01_1979-10-01_00:00:00.nc /glade/scratch/kyoko/USGS/conus404_production_outputs/OUTPUT/WY1980/wrf2d_d01_1979-10-01_00:00:00 Tue Mar 29 16:35:21 2022: ncrcat -A -vZ /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_Z_d01_1979-10-01_00:00:00.nc /glade/scratch/kyoko/USGS/conus404_production_outputs/OUTPUT/WY1980/wrf2d_d01_1979-10-01_00:00:00
- history_of_appended_files :
- Tue Mar 29 16:35:22 2022: Appended file /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_W_d01_1979-10-01_00:00:00.nc had no "history" attribute Tue Mar 29 16:35:21 2022: Appended file /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_Z_d01_1979-10-01_00:00:00.nc had no "history" attribute
First, calculate DNB and UPB
c404_hourly_dask_sub["DNB"] = c404_hourly_dask_sub['ACSWDNB'] - c404_hourly_dask_sub['ACLWDNB']
c404_hourly_dask_sub['UPB'] = c404_hourly_dask_sub['ACSWUPB'] - c404_hourly_dask_sub['ACLWUPB']
Then calculate FAKERAD.
c404_hourly_dask_sub['FAKERAD'] = c404_hourly_dask_sub['DNB'] - c404_hourly_dask_sub['UPB']
This has all happened so fast! But, has anything actually happened? Dask has been keeping track of the operations and won’t run them all until the compute() method is passed. The execution will take longer time than the last couple of cells but will be much faster than runner all of these operations on an in-memory dataset.
c404_hourly_memory_sub = c404_hourly_dask_sub.compute()
c404_hourly_memory_sub
<xarray.Dataset>
Dimensions: (time: 1, y: 1015, x: 1367)
Coordinates:
lat (y, x) float32 17.65 17.66 17.67 17.68 ... 51.74 51.73 51.71 51.69
lon (y, x) float32 -122.6 -122.5 -122.5 -122.5 ... -57.17 -57.12 -57.07
* time (time) datetime64[ns] 1979-10-01
* x (x) float64 -2.732e+06 -2.728e+06 ... 2.728e+06 2.732e+06
* y (y) float64 -2.028e+06 -2.024e+06 -2.02e+06 ... 2.024e+06 2.028e+06
Data variables:
ACSWDNB (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
ACSWUPB (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
ACLWDNB (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
ACLWUPB (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
DNB (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
UPB (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
FAKERAD (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
Attributes: (12/148)
AER_ANGEXP_OPT: 1
AER_ANGEXP_VAL: 1.2999999523162842
AER_AOD550_OPT: 1
AER_AOD550_VAL: 0.11999999731779099
AER_ASY_OPT: 1
AER_ASY_VAL: 0.8999999761581421
... ...
WEST-EAST_PATCH_START_STAG: 1
WEST-EAST_PATCH_START_UNSTAG: 1
W_DAMPING: 1
YSU_TOPDOWN_PBLMIX: 0
history: Tue Mar 29 16:35:22 2022: ncrcat -A -vW ...
history_of_appended_files: Tue Mar 29 16:35:22 2022: Appended file ...- time: 1
- y: 1015
- x: 1367
- lat(y, x)float3217.65 17.66 17.67 ... 51.71 51.69
- description :
- LATITUDE, SOUTH IS NEGATIVE
- long_name :
- Latitude, south is negative
- standard_name :
- latitude
- units :
- degree_north
array([[17.647308, 17.656631, 17.665955, ..., 17.665955, 17.656631, 17.647308], [17.679993, 17.689323, 17.698647, ..., 17.698647, 17.689323, 17.679993], [17.712677, 17.722015, 17.731354, ..., 17.731354, 17.722015, 17.712677], ..., [51.629955, 51.64582 , 51.661667, ..., 51.661667, 51.64582 , 51.629955], [51.662006, 51.67787 , 51.693737, ..., 51.693737, 51.67787 , 51.662006], [51.694042, 51.70993 , 51.72579 , ..., 51.72579 , 51.70993 , 51.694042]], dtype=float32) - lon(y, x)float32-122.6 -122.5 ... -57.12 -57.07
- description :
- LONGITUDE, WEST IS NEGATIVE
- long_name :
- Longitude, west is negative
- standard_name :
- longitude
- units :
- degree_east
array([[-122.57411 , -122.539825, -122.50552 , ..., -73.294495, -73.26019 , -73.22589 ], [-122.58391 , -122.54961 , -122.51529 , ..., -73.28473 , -73.2504 , -73.216095], [-122.59372 , -122.5594 , -122.52507 , ..., -73.27493 , -73.2406 , -73.2063 ], ..., [-138.68015 , -138.6285 , -138.57681 , ..., -57.223206, -57.17151 , -57.119873], [-138.70573 , -138.65405 , -138.60236 , ..., -57.197662, -57.145935, -57.09427 ], [-138.73135 , -138.67966 , -138.62793 , ..., -57.17206 , -57.12036 , -57.068634]], dtype=float32) - time(time)datetime64[ns]1979-10-01
- standard_name :
- time
array(['1979-10-01T00:00:00.000000000'], dtype='datetime64[ns]')
- x(x)float64-2.732e+06 -2.728e+06 ... 2.732e+06
- long_name :
- x coordinate of projection
- standard_name :
- projection_x_coordinate
- units :
- m
array([-2732097.901154, -2728097.901154, -2724097.901154, ..., 2723902.098846, 2727902.098846, 2731902.098846]) - y(y)float64-2.028e+06 -2.024e+06 ... 2.028e+06
- long_name :
- y coordinate of projection
- standard_name :
- projection_y_coordinate
- units :
- m
array([-2027960.899637, -2023960.899637, -2019960.899637, ..., 2020039.100363, 2024039.100363, 2028039.100363])
- ACSWDNB(time, y, x)float320.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
- description :
- ACCUMULATED DOWNWELLING SHORTWAVE FLUX AT BOTTOM
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated downwelling shortwave radiation flux at bottom
- units :
- J m-2
array([[[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32) - ACSWUPB(time, y, x)float320.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
- description :
- ACCUMULATED UPWELLING SHORTWAVE FLUX AT BOTTOM
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated upwelling shortwave radiation flux at bottom
- units :
- J m-2
array([[[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32) - ACLWDNB(time, y, x)float320.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
- description :
- ACCUMULATED DOWNWELLING LONGWAVE FLUX AT BOTTOM
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated downwelling longwave radiation flux at bottom
- units :
- J m-2
array([[[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32) - ACLWUPB(time, y, x)float320.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
- description :
- ACCUMULATED UPWELLING LONGWAVE FLUX AT BOTTOM
- grid_mapping :
- crs
- integration_length :
- accumulated since 1979-10-01 00:00:00
- long_name :
- Accumulated upwelling longwave radiation flux at bottom
- units :
- J m-2
array([[[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32) - DNB(time, y, x)float320.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
array([[[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32) - UPB(time, y, x)float320.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
array([[[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32) - FAKERAD(time, y, x)float320.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
array([[[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)
- timePandasIndex
PandasIndex(DatetimeIndex(['1979-10-01'], dtype='datetime64[ns]', name='time', freq=None))
- xPandasIndex
PandasIndex(Float64Index([-2732097.901153542, -2728097.901153542, -2724097.901153542, -2720097.901153542, -2716097.901153542, -2712097.901153542, -2708097.901153542, -2704097.901153542, -2700097.901153542, -2696097.901153542, ... 2695902.098846458, 2699902.098846458, 2703902.098846458, 2707902.098846458, 2711902.098846458, 2715902.098846458, 2719902.098846458, 2723902.098846458, 2727902.098846458, 2731902.098846458], dtype='float64', name='x', length=1367)) - yPandasIndex
PandasIndex(Float64Index([-2027960.8996368449, -2023960.8996368449, -2019960.8996368449, -2015960.8996368449, -2011960.8996368449, -2007960.8996368449, -2003960.8996368449, -1999960.8996368449, -1995960.8996368449, -1991960.8996368449, ... 1992039.1003631551, 1996039.1003631551, 2000039.1003631551, 2004039.1003631551, 2008039.1003631551, 2012039.1003631551, 2016039.1003631551, 2020039.1003631551, 2024039.1003631551, 2028039.1003631551], dtype='float64', name='y', length=1015))
- AER_ANGEXP_OPT :
- 1
- AER_ANGEXP_VAL :
- 1.2999999523162842
- AER_AOD550_OPT :
- 1
- AER_AOD550_VAL :
- 0.11999999731779099
- AER_ASY_OPT :
- 1
- AER_ASY_VAL :
- 0.8999999761581421
- AER_OPT :
- 1
- AER_SSA_OPT :
- 1
- AER_SSA_VAL :
- 0.8500000238418579
- AER_TYPE :
- 1
- BLDT :
- 0.0
- BL_PBL_PHYSICS :
- 1
- BOTTOM-TOP_GRID_DIMENSION :
- 51
- BOTTOM-TOP_PATCH_END_STAG :
- 51
- BOTTOM-TOP_PATCH_END_UNSTAG :
- 50
- BOTTOM-TOP_PATCH_START_STAG :
- 1
- BOTTOM-TOP_PATCH_START_UNSTAG :
- 1
- BUCKET_J :
- 1000000000.0
- BUCKET_MM :
- 100.0
- CEN_LAT :
- 39.100006103515625
- CEN_LON :
- -97.89999389648438
- CUDT :
- 5.0
- CU_PHYSICS :
- 0
- Contacts :
- CHANGHAI LIU (chliu@ucar.edu), KYOKO IKEDA (kyoko@ucar.edu)
- DAMPCOEF :
- 0.20000000298023224
- DAMP_OPT :
- 3
- DFI_OPT :
- 0
- DIFF_6TH_FACTOR :
- 0.11999999731779099
- DIFF_6TH_OPT :
- 0
- DIFF_OPT :
- 2
- DT :
- 20.0
- DTRAMP_MIN :
- 60.0
- DVEG :
- 9
- DX :
- 4000.0
- DY :
- 4000.0
- Division :
- NCAR/RAL/HAP
- ETAC :
- 0.0
- FEEDBACK :
- 1
- FGDT :
- 2.0
- FileGenerated :
- 20210204
- GFDDA_END_H :
- 999999
- GFDDA_INTERVAL_M :
- 180
- GMT :
- 0.0
- GPH :
- 4.999999873689376e-05
- GRAV_SETTLING :
- 0
- GRIDTYPE :
- C
- GRID_FDDA :
- 2
- GRID_ID :
- 1
- GRID_SFDDA :
- 0
- GT :
- 4.999999873689376e-05
- GUV :
- 4.999999873689376e-05
- GWD_OPT :
- 0
- HYBRID_OPT :
- -1
- HYPSOMETRIC_OPT :
- 2
- ICLOUD :
- 1
- ICLOUD_CU :
- 0
- IF_RAMPING :
- 1
- ISFFLX :
- 1
- ISFTCFLX :
- 0
- ISHALLOW :
- 0
- ISICE :
- 15
- ISLAKE :
- 21
- ISOILWATER :
- 14
- ISURBAN :
- 13
- ISWATER :
- 17
- I_PARENT_START :
- 1
- JULDAY :
- 274
- JULYR :
- 1979
- J_PARENT_START :
- 1
- KHDIF :
- 0.0
- KM_OPT :
- 4
- KVDIF :
- 0.0
- MAP_PROJ :
- 1
- MAP_PROJ_CHAR :
- Lambert Conformal
- MFSHCONV :
- 0
- MMINLU :
- MODIFIED_IGBP_MODIS_NOAH
- MOAD_CEN_LAT :
- 39.100006103515625
- MOIST_ADV_OPT :
- 1
- MP_PHYSICS :
- 8
- NCO :
- netCDF Operators version 4.9.5 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)
- NUM_LAND_CAT :
- 21
- OBS_NUDGE_OPT :
- 0
- OPT_ALB :
- 2
- OPT_BTR :
- 2
- OPT_CRS :
- 1
- OPT_FRZ :
- 1
- OPT_GLA :
- 1
- OPT_INF :
- 1
- OPT_RAD :
- 3
- OPT_RSF :
- 1
- OPT_RUN :
- 5
- OPT_SFC :
- 1
- OPT_SNF :
- 4
- OPT_STC :
- 3
- OPT_TBOT :
- 1
- PARENT_GRID_RATIO :
- 1
- PARENT_ID :
- 0
- POLE_LAT :
- 90.0
- POLE_LON :
- 0.0
- PREC_ACC_DT :
- 60.0
- Project :
- USGS CONUS404
- RADT :
- 5.0
- RA_LW_PHYSICS :
- 4
- RA_SW_PHYSICS :
- 4
- SCALAR_ADV_OPT :
- 1
- SCALAR_PBLMIX :
- 0
- SF_LAKE_PHYSICS :
- 0
- SF_OCEAN_PHYSICS :
- 0
- SF_SFCLAY_PHYSICS :
- 1
- SF_SURFACE_PHYSICS :
- 4
- SF_URBAN_PHYSICS :
- 0
- SGFDDA_END_H :
- 0
- SGFDDA_INTERVAL_M :
- 0
- SHCU_PHYSICS :
- 0
- SIMULATION_INITIALIZATION_TYPE :
- REAL-DATA CASE
- SIMULATION_START_DATE :
- 1979-10-01_00:00:00
- SKEBS_ON :
- 0
- SMOOTH_OPTION :
- 2
- SOUTH-NORTH_GRID_DIMENSION :
- 1016
- SOUTH-NORTH_PATCH_END_STAG :
- 1016
- SOUTH-NORTH_PATCH_END_UNSTAG :
- 1015
- SOUTH-NORTH_PATCH_START_STAG :
- 1
- SOUTH-NORTH_PATCH_START_UNSTAG :
- 1
- SPEC_BDY_FINAL_MU :
- 1
- SST_UPDATE :
- 1
- STAND_LON :
- -97.9000015258789
- START_DATE :
- 1979-10-01_00:00:00
- SURFACE_INPUT_SOURCE :
- 1
- SWINT_OPT :
- 0
- SWRAD_SCAT :
- 1.0
- Source_Code :
- make_conusii_2d.csh
- TITLE :
- OUTPUT FROM WRF V3.9.1.1 MODEL
- TKE_ADV_OPT :
- 1
- TOPO_WIND :
- 1
- TRACER_PBLMIX :
- 1
- TRUELAT1 :
- 30.0
- TRUELAT2 :
- 50.0
- USE_Q_DIABATIC :
- 0
- USE_THETA_M :
- 0
- WEST-EAST_GRID_DIMENSION :
- 1368
- WEST-EAST_PATCH_END_STAG :
- 1368
- WEST-EAST_PATCH_END_UNSTAG :
- 1367
- WEST-EAST_PATCH_START_STAG :
- 1
- WEST-EAST_PATCH_START_UNSTAG :
- 1
- W_DAMPING :
- 1
- YSU_TOPDOWN_PBLMIX :
- 0
- history :
- Tue Mar 29 16:35:22 2022: ncrcat -A -vW /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_W_d01_1979-10-01_00:00:00.nc /glade/scratch/kyoko/USGS/conus404_production_outputs/OUTPUT/WY1980/wrf2d_d01_1979-10-01_00:00:00 Tue Mar 29 16:35:21 2022: ncrcat -A -vZ /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_Z_d01_1979-10-01_00:00:00.nc /glade/scratch/kyoko/USGS/conus404_production_outputs/OUTPUT/WY1980/wrf2d_d01_1979-10-01_00:00:00
- history_of_appended_files :
- Tue Mar 29 16:35:22 2022: Appended file /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_W_d01_1979-10-01_00:00:00.nc had no "history" attribute Tue Mar 29 16:35:21 2022: Appended file /glade/scratch/kyoko/USGS/conus404_production_outputs/GHT/WY1980/CONUS404_Z_d01_1979-10-01_00:00:00.nc had no "history" attribute
If you are working with large tabular data, Dask Dataframes performs the same way and is modeled after pandas dataframes. The .compute() graphs can be visualized for these and more information can be found here.
c404_hourly_dask_sub['ACSWDNB'].data.visualize()
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
Cell In[19], line 1
----> 1 c404_hourly_dask_sub['ACSWDNB'].data.visualize()
File /home/conda/users/ef214d1986de9477d5223419ba4838d76e9839b19e4fb02c9e208c97cab5a61b-20230329-131713-443303-165-pangeofu/lib/python3.10/site-packages/dask/base.py:240, in DaskMethodsMixin.visualize(self, filename, format, optimize_graph, **kwargs)
196 def visualize(self, filename="mydask", format=None, optimize_graph=False, **kwargs):
197 """Render the computation of this object's task graph using graphviz.
198
199 Requires ``graphviz`` to be installed.
(...)
238 https://docs.dask.org/en/latest/optimize.html
239 """
--> 240 return visualize(
241 self,
242 filename=filename,
243 format=format,
244 optimize_graph=optimize_graph,
245 **kwargs,
246 )
File /home/conda/users/ef214d1986de9477d5223419ba4838d76e9839b19e4fb02c9e208c97cab5a61b-20230329-131713-443303-165-pangeofu/lib/python3.10/site-packages/dask/base.py:796, in visualize(filename, traverse, optimize_graph, maxval, engine, *args, **kwargs)
793 if engine == "graphviz":
794 from dask.dot import dot_graph
--> 796 return dot_graph(dsk, filename=filename, **kwargs)
797 elif engine in ("cytoscape", "ipycytoscape"):
798 from dask.dot import cytoscape_graph
File /home/conda/users/ef214d1986de9477d5223419ba4838d76e9839b19e4fb02c9e208c97cab5a61b-20230329-131713-443303-165-pangeofu/lib/python3.10/site-packages/dask/dot.py:274, in dot_graph(dsk, filename, format, **kwargs)
236 """
237 Render a task graph using dot.
238
(...)
271 dask.dot.to_graphviz
272 """
273 g = to_graphviz(dsk, **kwargs)
--> 274 return graphviz_to_file(g, filename, format)
File /home/conda/users/ef214d1986de9477d5223419ba4838d76e9839b19e4fb02c9e208c97cab5a61b-20230329-131713-443303-165-pangeofu/lib/python3.10/site-packages/dask/dot.py:291, in graphviz_to_file(g, filename, format)
288 if format is None:
289 format = "png"
--> 291 data = g.pipe(format=format)
292 if not data:
293 raise RuntimeError(
294 "Graphviz failed to properly produce an image. "
295 "This probably means your installation of graphviz "
(...)
298 "issues/485 for more information."
299 )
File /home/conda/users/ef214d1986de9477d5223419ba4838d76e9839b19e4fb02c9e208c97cab5a61b-20230329-131713-443303-165-pangeofu/lib/python3.10/site-packages/graphviz/piping.py:104, in Pipe.pipe(self, format, renderer, formatter, neato_no_op, quiet, engine, encoding)
55 def pipe(self,
56 format: typing.Optional[str] = None,
57 renderer: typing.Optional[str] = None,
(...)
61 engine: typing.Optional[str] = None,
62 encoding: typing.Optional[str] = None) -> typing.Union[bytes, str]:
63 """Return the source piped through the Graphviz layout command.
64
65 Args:
(...)
102 '<?xml version='
103 """
--> 104 return self._pipe_legacy(format,
105 renderer=renderer,
106 formatter=formatter,
107 neato_no_op=neato_no_op,
108 quiet=quiet,
109 engine=engine,
110 encoding=encoding)
File /home/conda/users/ef214d1986de9477d5223419ba4838d76e9839b19e4fb02c9e208c97cab5a61b-20230329-131713-443303-165-pangeofu/lib/python3.10/site-packages/graphviz/_tools.py:171, in deprecate_positional_args.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
162 wanted = ', '.join(f'{name}={value!r}'
163 for name, value in deprecated.items())
164 warnings.warn(f'The signature of {func.__name__} will be reduced'
165 f' to {supported_number} positional args'
166 f' {list(supported)}: pass {wanted}'
167 ' as keyword arg(s)',
168 stacklevel=stacklevel,
169 category=category)
--> 171 return func(*args, **kwargs)
File /home/conda/users/ef214d1986de9477d5223419ba4838d76e9839b19e4fb02c9e208c97cab5a61b-20230329-131713-443303-165-pangeofu/lib/python3.10/site-packages/graphviz/piping.py:121, in Pipe._pipe_legacy(self, format, renderer, formatter, neato_no_op, quiet, engine, encoding)
112 @_tools.deprecate_positional_args(supported_number=2)
113 def _pipe_legacy(self,
114 format: typing.Optional[str] = None,
(...)
119 engine: typing.Optional[str] = None,
120 encoding: typing.Optional[str] = None) -> typing.Union[bytes, str]:
--> 121 return self._pipe_future(format,
122 renderer=renderer,
123 formatter=formatter,
124 neato_no_op=neato_no_op,
125 quiet=quiet,
126 engine=engine,
127 encoding=encoding)
File /home/conda/users/ef214d1986de9477d5223419ba4838d76e9839b19e4fb02c9e208c97cab5a61b-20230329-131713-443303-165-pangeofu/lib/python3.10/site-packages/graphviz/piping.py:161, in Pipe._pipe_future(self, format, renderer, formatter, neato_no_op, quiet, engine, encoding)
159 else:
160 return raw.decode(encoding)
--> 161 return self._pipe_lines(*args, input_encoding=self.encoding, **kwargs)
File /home/conda/users/ef214d1986de9477d5223419ba4838d76e9839b19e4fb02c9e208c97cab5a61b-20230329-131713-443303-165-pangeofu/lib/python3.10/site-packages/graphviz/backend/piping.py:161, in pipe_lines(engine, format, input_lines, input_encoding, renderer, formatter, neato_no_op, quiet)
155 cmd = dot_command.command(engine, format,
156 renderer=renderer,
157 formatter=formatter,
158 neato_no_op=neato_no_op)
159 kwargs = {'input_lines': (line.encode(input_encoding) for line in input_lines)}
--> 161 proc = execute.run_check(cmd, capture_output=True, quiet=quiet, **kwargs)
162 return proc.stdout
File /home/conda/users/ef214d1986de9477d5223419ba4838d76e9839b19e4fb02c9e208c97cab5a61b-20230329-131713-443303-165-pangeofu/lib/python3.10/site-packages/graphviz/backend/execute.py:79, in run_check(cmd, input_lines, encoding, quiet, **kwargs)
77 if kwargs.pop('capture_output'):
78 kwargs['stdout'] = kwargs['stderr'] = subprocess.PIPE
---> 79 proc = _run_input_lines(cmd, input_lines, kwargs=kwargs)
80 else:
81 proc = subprocess.run(cmd, **kwargs)
File /home/conda/users/ef214d1986de9477d5223419ba4838d76e9839b19e4fb02c9e208c97cab5a61b-20230329-131713-443303-165-pangeofu/lib/python3.10/site-packages/graphviz/backend/execute.py:105, in _run_input_lines(cmd, input_lines, kwargs)
102 for line in input_lines:
103 stdin_write(line)
--> 105 stdout, stderr = popen.communicate()
106 return subprocess.CompletedProcess(popen.args, popen.returncode,
107 stdout=stdout, stderr=stderr)
File /home/conda/users/ef214d1986de9477d5223419ba4838d76e9839b19e4fb02c9e208c97cab5a61b-20230329-131713-443303-165-pangeofu/lib/python3.10/subprocess.py:1154, in Popen.communicate(self, input, timeout)
1151 endtime = None
1153 try:
-> 1154 stdout, stderr = self._communicate(input, endtime, timeout)
1155 except KeyboardInterrupt:
1156 # https://bugs.python.org/issue25942
1157 # See the detailed comment in .wait().
1158 if timeout is not None:
File /home/conda/users/ef214d1986de9477d5223419ba4838d76e9839b19e4fb02c9e208c97cab5a61b-20230329-131713-443303-165-pangeofu/lib/python3.10/subprocess.py:2005, in Popen._communicate(self, input, endtime, orig_timeout)
1998 self._check_timeout(endtime, orig_timeout,
1999 stdout, stderr,
2000 skip_check_and_raise=True)
2001 raise RuntimeError( # Impossible :)
2002 '_check_timeout(..., skip_check_and_raise=True) '
2003 'failed to raise TimeoutExpired.')
-> 2005 ready = selector.select(timeout)
2006 self._check_timeout(endtime, orig_timeout, stdout, stderr)
2008 # XXX Rewrite these to use non-blocking I/O on the file
2009 # objects; they are no longer using C stdio!
File /home/conda/users/ef214d1986de9477d5223419ba4838d76e9839b19e4fb02c9e208c97cab5a61b-20230329-131713-443303-165-pangeofu/lib/python3.10/selectors.py:416, in _PollLikeSelector.select(self, timeout)
414 ready = []
415 try:
--> 416 fd_event_list = self._selector.poll(timeout)
417 except InterruptedError:
418 return ready
KeyboardInterrupt:
Finally, here are some examples of calling in some datasets from various open access sources.
OpenDAP data#
Now, looking at CERES-EBAF, called in from an OpenDAP source.
# ceres-ebaf url
ceres_url = "https://opendap.larc.nasa.gov/opendap/CERES/EBAF/Edition4.1/CERES_EBAF_Edition4.1_200003-202111.nc"
# bring in ceres-ebaf
ceres = xr.open_dataset(ceres_url, decode_coords="all")
ceres
Data on a THREDD server#
# open daymet lazily
# this happens by setting chunks={}
# though you can specify chunks where applicable
daymet = xr.open_dataset("https://thredds.daac.ornl.gov/thredds/dodsC/daymet-v4-agg/na.ncml", chunks={})
daymet
Clipping xarray datasets to a bounding box#
Here are two possible ways to clip to a bounding box and when each might suit you best. Why a bounding box and not an exact clip? If storing an intermediate dataset, this makes the data smaller as well as giving flexibility to exact area extraction or only including cells inside of the area of interest.
The first is using the xarray index methods. This is beneficial because it doesn’t require using extra libraries but there have been isues when the spatial resolution of the dataset is very large and the geographical area is small. In some cases, it will drop some grid cells compared to the second method.
from pygeohydro import WBD
from cartopy import crs as ccrs
# bring in boundaries of DRB and create single polygon
drb = WBD("huc6", outfields=["huc6", "name"]).byids("huc6", ["020401", "020402"])
# create a column where all entries have the same value
drb["name"] = "DRB"
# dissolve by that column
drb = drb.dissolve(by="name")
# set CRS to match ds
drb = drb.iloc[[0]].to_crs(ccrs.LambertConformal())
# tuple of bounding box
drb_bbox = list(drb.total_bounds)
drb_bbox
daymet_sel = daymet.sel(x=slice(drb_bbox[1],drb_bbox[3]), y=slice(drb_bbox[2],drb_bbox[0]))
daymet_sel
In the above, the indexing returned a subset along the x dimension but nothing along the y due to underlying CRS/indexing conflicts.
The second is the rioxarray.clip_box method. rioxarray is used to apply rasterio to xarray and adds a rio accessor to xarray objects and documentation can be found here about rioxarray.. This method does require an additional library but does ensure that all grid cells that are inside the bounding box are included.
import rioxarray
# add crs to dataset
daymet.rio.write_crs(ccrs.LambertConformal(), inplace=True)
# drop time_bnds, otherwise it will throw an error in the clip_box
daymet = daymet.drop(["time_bnds"])
#clip to bbox
daymet_bbox = daymet.rio.clip_box(minx=drb_bbox[0],
miny=drb_bbox[1],
maxx=drb_bbox[2],
maxy=drb_bbox[3],
crs=ccrs.LambertConformal())
daymet_bbox
As can be seen, the indexing works great but does have one less grid cell (see y dim)
print("Index select:\n", daymet_sel.dims)
print("Rioxarray:\n", daymet_bbox.dims)
Finally, it is always important to shutdown the client and cluster.
client.close(); cluster.shutdown()